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A SERS And SEM-EDX Study of the Antiviral Mechanism of A SERS And SEM-EDX Study of the Antiviral Mechanism of
Creighton Silver Nanoparticles against Vaccinia Virus Creighton Silver Nanoparticles against Vaccinia Virus
Catherine Binns Anders Wright State University
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A SERS and SEM-EDX Study of the Antiviral Mechanism of
Creighton Silver Nanoparticles against Vaccinia Virus
A thesis submitted in partial fulfillment
of the requirements for the degree of
Master of Science
By
CATHERINE BINNS ANDERS
B.A., Shepherd University, 1988
2012
Wright State University
WRIGHT STATE UNIVERSITY SCHOOL OF GRADUATE STUDIES
Date: June 4, 2012
I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY
SUPERVISION BY Catherine Binns Anders ENTITLED “A SERS and SEM-EDX
Study of the Antiviral Mechanism of Creighton Silver Nanoparticles against
Vaccinia Virus” BE ACCEPTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF Master of Science.
________________________ Ioana E. Sizemore, Ph.D. Thesis Advisor
________________________ Kenneth Turnbull, Ph.D., Chair Department of Chemistry
Committee on Final Examination __________________________ David D. Dolson, Ph.D. __________________________ Steven R. Higgins, Ph.D. __________________________ Ioana E. Sizemore, Ph.D. __________________________ Andrew Hsu, Ph.D. Dean, School of Graduate Studies
iii
Abstract
Anders, Catherine Binns. M.S., Department of Chemistry, Wright State University,
2012. A SERS and SEM-EDX Study of the Antiviral Mechanism of Creighton Silver
Nanoparticles against Vaccinia Virus
Silver nanoparticles (AgNPs) are well-recognized as antiviral agents but
little is known about their mechanism of action. In this study, it was hypothesized
that unfunctionalized, Creighton AgNPs of an average diameter of 11 nm will
form covalent bonds with vaccinia virus (VV) mainly through the external, entry
fusion complex (EFC) proteins. The EFC is housed on the external membrane of
VV and contains 9-12 proteins having numerous cysteine groups, intramolecular
disulfide bonds, aromatic moieties and myristic acids bound to the N-terminus of
glycine residues. VV (1012 PFUs) was incubated at 37oC for one hour with
Creighton AgNPs that were size selected (1-25 nm in diameter) and
concentrated (1,000 ppm silver) using tangential flow ultrafiltration. After
incubation, the sample was rinsed three times to remove any unbound AgNPs
from the VV. The VV-AgNP sample was then deactivated with formaldehyde and
fixed onto glass slides and stubs for surface-enhanced Raman spectroscopy
(SERS) and scanning electron microscopy-energy dispersive X-Ray (SEM-EDX)
analysis, respectively. SERS maps containing over 2,600 spectra were collected
and processed using in-house written MatLab codes. Six endmember spectra
were extracted from the hyperspectral data set using a multivariate statistical
analysis method, namely vector component analysis (VCA). The SERS analysis
iv
of the six endmember spectra indicated an interaction trend similar to that
reported in literature by other SERS studies on proteins exposed to AgNPs:
carboxylic groups > peptide bond interactions (amide peaks) > aromatic AAs >
thiol groups > small side chains. Additionally, the SEM electron backscatter
images and the EDX spectrum of the VV-AgNP sample revealed the presence of
silver and further supported the direct interaction between AgNPs and VV. These
interactions confirmed the proposed hypothesis and suggested that the covalent
bonding interactions might disrupt the VV ability to complete the entry/fusion
steps of the viral replication cycle.
v
Table of Contents
INTRODUCTION .................................................................................................. 1
Smallpox and the Vaccinia Virus ................................................................... 1
Silver nanoparticles (AgNPs) ......................................................................... 3
Surface-enhanced Raman spectroscopy (SERS) and scanning electron
microscopy – energy dispersive X-ray (SEM-EDX) ....................................... 8
Hypothesis ................................................................................................... 10
Specific aims ............................................................................................... 10
TECHNICAL APPROACH ................................................................................. 11
Specific Aim 1: .................................................................................................. 11
Reagent solutions and glassware preparation ................................................ 11
Creighton synthesis of silver nanoparticles ..................................................... 11
Characterization of silver nanoparticles .......................................................... 13
UV-Vis Absorption Spectrophotometry ........................................................ 13
Micro-Raman Spectroscopy ........................................................................ 16
Tangential Flow Ultrafiltration (TFU) ............................................................ 17
Inductively Coupled Plasma - Optical Emission Spectroscopy (ICP-OES) .. 21
vi
Scanning Electron Microscopy – Energy-Dispersive X-Ray (SEM-EDX) and
Transmission Electron Microscopy (TEM) ................................................... 24
Specific Aim 2: .................................................................................................. 28
Virus Sample Preparation ............................................................................... 28
Micro-Raman and Surface-enhanced Raman Spectroscopy (SERS) ............. 28
Hyperspectroscopy and Multivariate Analysis Methods .................................. 36
RESULTS AND DISCUSSION ........................................................................... 47
Specific Aim 1: .................................................................................................. 47
Synthesis of silver nanoparticles ..................................................................... 47
Characterization of silver nanoparticles .......................................................... 47
UV-Vis absorption spectrophotometry ......................................................... 47
Tangential flow ultrafiltration (TFU) ............................................................. 51
Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) .... 53
Transmission electron microscopy (TEM) ................................................... 57
Elemental analysis characterization using SEM-EDX ................................. 59
Micro-Raman Spectroscopy ........................................................................ 61
Specific Aim 2: .................................................................................................. 64
Vaccinia Virus Control ..................................................................................... 64
Background information about Vaccinia Virus (VV) ..................................... 64
vii
Estimation of the VV external surface coverage with AgNPs ...................... 69
Elemental analysis characterization using SEM-EDX ................................. 71
Micro-Raman Spectroscopy ........................................................................ 73
Vaccinia Virus treated with 1,000 g mL-1 of AgNPs ....................................... 74
Elemental analysis ...................................................................................... 74
Background Information on Surface-enhanced Raman spectroscopy on
Viruses ........................................................................................................ 77
Surface-enhanced Raman spectroscopy on Creighton silver nanoparticles
interacting with vaccinia virus ...................................................................... 83
Covalent bonding interactions of silver with vaccinia virus .......................... 87
SERS spectra correlation ............................................................................ 92
Conclusions ...................................................................................................... 97
REFERENCES ................................................................................................... 98
viii
List of Figures
Figure 1: A) Creighton colloid synthesis setup and B) Characteristic
golden-yellow color of the Creighton colloidal AgNPs. ....................... 12
Figure 2: Allowed electronic transitions for UV-VIS absorption: A)
to *, B) n to * and C) n to *. ............................................................ 14
Figure 3: Cary 50 UV-VIS-NIR spectrophotometer from Varian Inc.
used to measure the SPR peak of the AgNP colloid. ......................... 16
Figure 4: TFU illustration of the selection process through the
hollow fiber membrane. (Used with permission from
Joshua D. Baker) ................................................................................ 18
Figure 5: Schematic illustrating the TFU working principle: a-c
represent sucessive recirculating passes through the
TFU system. ....................................................................................... 19
Figure 6: KrosFlo II TFU system used for size selection and
concentration of the AgNP colloid. ..................................................... 20
Figure 7: Excitation and emission process. A - D correspond
various levels of excitation energy: A and B) Atomic
excitation; C) Ionization and D) Ionization and excitation.
λ1 – λ4 represent characteristic emission patterns: λ1, λ2
and λ3) Atomic emissions and λ4) Ionic emission.41 ............................ 21
Figure 8: 710E ICP-OES (Varian Inc.) used to quantify the amount
of silver present in each colloidal sample. .......................................... 24
Figure 9: Jeol JIB 4500 scanning electron microscope used for
SEM-EDX measurements. ................................................................. 27
Figure 10: Virtual energy state attained by excited electrons ............................. 29
Figure 11: Fundamental principle of Raman spectroscopy: a)
Stokes scattering, b) Rayleigh scattering and c) Anti-
Stokes scattering.48 ............................................................................ 30
ix
Figure 12: Schematic of the electromagnetic enhancement
mechanism in SERS. .......................................................................... 32
Figure 13: Microscope slide preparation for virus samples. ................................ 34
Figure 14: LabRamHR 800 Raman system (Horiba Jobin Yvon,
Inc.) used to verify AgNP colloid purity and analyze virus
samples. ............................................................................................. 36
Figure 15: Hyperspectral dataset representations. The images in
the left and right columns represent the spatial orientation
of the data selection and the corresponding spectral
image taken from hyperspectral data obtained from
micro-Raman analysis of the cross section of chick
embryo bones. A) Spatial selection is equivalent to point
spectrum measurements; B) Several point spectrum
measurements corresponding spatially to a data obtained
from a linear section across the sample surface; C) An
RGB image obtained from the analysis of the signal
response obtained at that specific wavenumber from the
spectral axis for all 2D points on the sample surface.38. ..................... 38
Figure 16: Geometric representation of PCs54. ................................................... 42
Figure 17: UV-Vis absorption spectrum of the colloidal AgNPs. ......................... 49
Figure 18: Schematic illustrating the three-step TFU process. The
green-shaded boxes indicate the AgNP colloidal samples
retained for analysis. Vial photographs show A) Original
colloid, B) 50 nm filtrate collected after processing the
original colloid through the through the 50 nm filter, C)
100-kD retentate obtained after the first volume reduction
using the 100-kD MidiKros filter (200 cm2) and D) 100-kD
final retentate resulting from the second volume reduction
using the 100-kD MicroKros filter (20 cm2). ........................................ 52
Figure 19: IPC-OES calibration curve prepared from eight Ag
standards (0, 3, 7, 10, 15, 25, 50, and 100 μg L-1). ............................ 53
x
Figure 20: TEM micrographs and TEM size distribution histograms
of original Creighton AgNP colloid (A and B) and final 100
kD AgNP colloidal suspension (C and D). The inset on
the original colloid histogram (B) represents an expanded
view of the 41-75 nm size range for comparison. The size
bar is 100 nm for the TEM micrographs30 ........................................... 57
Figure 21: SEM-EDX results for 100-kD retentate of AgNPs. A, B,
and C represent the SEM images with an acceleration
voltage of 10 kV for A and B and 15kV for C. D depicts
the EDX spectrum corresponding to image C. ................................... 59
Figure 22: Raman spectrum of AgNP colloid. ..................................................... 61
Figure 23: Micro-Raman spectrum containing the three principal
components accounting for 99.6% of the variance in the
hyperspectral dataset (N = 100 spectra). ........................................... 62
Figure 24: Pictorial representation of the vaccinia virus. .................................... 65
Figure 25: Myristoylated terminal moiety of a protein. ........................................ 66
Figure 26: A summary of the AA composition of the nine EFC
proteins residing outside of the external membrane of
VV. ...................................................................................................... 68
Figure 27: SEM-EDX results for VV control. A, B, and C represent
SEM images collected with acceleration voltages of 15
kV, 20 kV, and 20 kV, respectively. D depicts the EDX
spectrum corresponding to image C. .................................................. 72
Figure 28: Micro-Raman point spectra (N = 20) of virus control
samples in comparison with a point spectrum collected
from the glass microscope slide control. ............................................. 74
Figure 29: SEM-EDX results for VV- AgNP sample. A depicts the
electron backscatter SEM image using an acceleration
voltage of 15 kV. B, C and D represent SEM images
collected with an acceleration voltage of 15 kV and the
corresponding EDX spectrum for the VV-AgNP control,
respectively. ....................................................................................... 75
xi
Figure 30: Six representative normalized SERS spectra (A-F) as
selected by VCA and correlation algorithms. ...................................... 84
Figure 31: Six representative normalized SERS spectra (A-F)
together with the primary interactions (carboxylic groups,
amide groups, aromatic amino acids and thiol groups)
ranked in order from most frequent to least often. .............................. 93
xii
List of Tables
Table 1: Percent cumulative variance determined by increasing
PCA model size37. .............................................................................. 41
Table 2: Silver amounts determined by ICP-OES for the
representative TFU samples of two independent bacthes:
A) Original colloid, B) Initial 100-kD retentate, and C)
Final 100-kD retentate. ....................................................................... 54
Table 3: Percentage weight of elements present in the final 100-kD
retentate of AgNPs as revealed by EDX analysis. .............................. 60
Table 4: Structural differences in the nine EFC proteins relevant to
the interaction mechanism with AgNPs. The total number
of AAs in the sequence of each EFC protein is given in
parenthesis. ........................................................................................ 67
Table 5: Percentage weight of elements present in the VV control
sample as revealed by the EDX analysis. ......................................... 73
Table 6: Percentage weight of elements present in the VV-AgNP
sample as revealed by EDX analysis. ................................................ 77
Table 7: Viruses investigated by SERS and the used
nanosubstrate. .................................................................................... 80
Table 8: Literature assignment of the SERS peaks observed during
the interaction of various viruses with silver and gold
nanosubstrates. .................................................................................. 81
Table 9: Tentative assignment of the SERS peaks in the 100-1000
cm-1 spectral range of the six, representative EMs
(denoted with A-F in Figure 30). ......................................................... 85
Table 10: Tentative assignment of the SERS peaks in the 1000-
2000 cm-1 spectral range of the six representative EMs
(denoted with A-F in Figure 30). ......................................................... 86
xiii
Table 11: Spectral correlation of EMs A-F to the compiled,
hyperspectral dataset (392 spectra). The values
represent the number of spectra in the compiled dataset
that contain similar spectral characteristics to the
representative EMs at specific correlation thresholds......................... 95
xiv
Acknowledgements
First and foremost I would like to thank Dr. Ioana Pavel Sizemore for her
tireless belief in this project and guidance both academically and professionally.
Also to the members of Dr. Sizemore’s research group for their help in making
this project possible. Specific thanks to Adam Stahler for numerous hours writing
MatLab codes and to Josh Baker for the countless hours spent filtering
nanoparticles. I would also like to acknowledge the WSU chemistry department
and the guidance of Dr. David Dolson and Dr. Steven Higgins.
Special thanks also go to Dr. John Trefry and Dr. Dawn Wooley for the
generous donation of the vaccinia virus stock and to Dr. Virgil Solomon at
Youngstown State University for his expertise and assistance with the SEM-EDX
measurements.
Finally, endless appreciation goes to my husband and children for
believing in me and standing with me. I couldn’t have done this without you.
1
Introduction
Smallpox and the Vaccinia Virus
Smallpox related diseases date back as many as 10,000 years to the
ancient civilizations of Mesopotamia and regions near Egypt, and eventually
spread to other civilizations1-2 . During the latter two centuries, smallpox had
caused epidemics and globally killed over 500 million people1. There are two
primary infectious forms of smallpox: variola major and variola minor. Variola
major, the most dangerous form of the disease, is estimated to be fatal in 30-
35% of infections2. The quest to eradicate smallpox as a disease began in 1958
and continued tirelessly until smallpox was declared eradicated worldwide on
October 26, 1979. This was two years from the last reported smallpox case, a
day known as smallpox zero day2.
After its eradication, the smallpox virus was contained to two secure
locations within the world: the first being the Centers for Disease Control and
Prevention, in Atlanta, GA, and the second known as the State Research
Institute of Virology and Biotechnology (also known as Vector) in Novosibirsk,
Siberia. However, it is believed that since the containment of the virus, viral
stocks have landed in the hands of many nations linked to terrorist regimes and
hostile leadership including Iraq and North Korea3.
2
Other more prevalent orthopoxviruses, such as monkey-pox virus (MPV)
also present challenges and potential threats as bioterrorist agents. MPV, which
causes smallpox-like infections in primates, could be especially problematic
considering the evolutionary similarities between primates and humans3-4.
Furthermore, remaining supplies of smallpox vaccine are limited and would only
immunize approximately 10% of the world’s population. The implementation of a
large scale vaccination program in the event of a bioterrorist threat or large scale
cross-species orthopoxvirus infections would be ineffective in preventing
potential pandemic scenarios1.
Antiviral drugs offer the best options to mitigate the effects of widespread
viral infections. However, most antivirals are agent specific and no antiviral drugs
have been tested for use with poxvirus infections5. There is a great need for the
development of a broad-spectrum antiviral treatment that can be used against a
variety of viral agents. Silver nanoparticles (AgNPs) have a proven history of
antimicrobial properties against a variety of biological agents6-9 including LIVP
strain of smallpox (SPV)10 and vaccinia virus5. Experimentation with the actual
smallpox virus is not possible today given the protected status of variola major
and variola minor. Vaccinia virus (VV), the gold standard for the development of
the smallpox vaccine, is readily available and is an effective laboratory model for
the study of smallpox and related orthopoxviruses11-12.
3
Silver nanoparticles (AgNPs)
The preparation and characterization of nanostructures (1-100 nm in at
least one dimension) has been a very active sector for the past decades primarily
due to their unique physical, chemical and antimicrobial properties13,6-7. In
general, colloidal AgNPs exist as zerovalent metal particles, and have low
coordination numbers and highly polarized surfaces14-15. Seeking a more stable
environment, colloidal AgNPs will preferentially bind to organic and biological
molecules through covalent interactions with common electron donors such as
oxygen, nitrogen, thiol and phosphate groups14. The small size of nanomaterials
offers many advantages in the commercial sector due to large surface area to
volume ratios yet their toxicity is often much lower to their bulk or ionic
counterparts16. This has led to exponential growth in the use of AgNPs in
consumer products and biomedical applications in the past decade.
Nanomaterials have also received considerable attention in many research areas
including environmental pollutants17, biomedical devices6, electronics18,
pharmaceutical products7 and sensors19-21.
The antimicrobial properties of AgNPs have been extensively studied
against a variety of biological agents including bacteria6-7, fungi7 and numerous
viruses (e.g., HIV-18,22, hepatitis B23, herpes simplex virus (HSV)24, 9, monkey-pox
virus4, tacaribe virus25, LIVP strain of smallpox (SPV)10 and vaccinia virus (VV)5).
Much of the virus-related research has focused on the size-dependent
relationship of the NPs and their level of antiviral activity.
4
Elechiguerra et.al found that AgNPs (1-10 nm in diameter) synthesized
using three different methods demonstrated antiviral capabilities against the HIV-
1 virus. The first formulation consisted of non-spherical AgNPs obtained from
Nanotechnologies, Inc. that were embedded in a foamy carbon matrix. Ultra-
sonication and a TEM electron beam were employed to disrupt the bonds
between the NPs and carbon, and to release the majority of the AgNPs from the
matrix8. The other two formulations were poly (N-vinyl-2-pyrrolidone) (PVP)
coated AgNPs and bovine serum albumin (BSA) conjugated AgNPs. High angle
annular dark field (HAADF) scanning transmission electron microscopy
demonstrated that the AgNPs bound to the GP-120 glycoprotein knobs
protruding from the external membrane of the HIV-1 virus8. Furthermore, the
HAADF images revealed an ordered distribution of the AgNPs suggesting that
the NPs bound preferentially to the GP-120 glycoprotein knobs, which were
spaced approximately 22 nm apart8. All three formulations showed antiviral
activity at concentrations above 25 g mL-1. However, the AgNPs that had been
released from the foamy carbon matrix (i.e., unfunctionalized AgNPs) exhibited
the greatest antiviral activity8. This increase in inhibition was attributed to the
ability of the AgNPs to bind directly to the GP-120 glycoprotein knobs without
bonding interferences from the functional groups (i.e., PVP and BSA) present in
the other formulations8.
Rogers et al. examined the antiviral effects of different AgNP formulations
(polysaccharide-coated and non-coated) and sizes (10-80 nm) against monkey-
5
pox virus (MPV) in conjunction with African green monkey (Vero) kidney cells
using a plaque reduction assay. Both AgNP types demonstrated antiviral activity
against MPV at concentrations as low as 12.5 g mL-1. However, only the 10 nm
polysaccharide-coated AgNPs exhibited consistent plaque reduction capabilities,
and suggested a size-dependent antiviral activity 4.
Bogdanchikova et al. investigated the size-dependent antiviral effects of
two different medicinal preparations of unfunctionalized AgNPs (i.e., collargol and
protargol) against SPV. The different formulations had AgNPs of average
diameters of 10 nm and 2 nm, respectively10. The 10 nm collargol AgNPs were
found to have a larger antiviral activity against SPV than the 2 nm protargol
AgNPs10. The author concluded that the large surface area to volume ratios of
the smaller AgNPs resulted in the rapid oxidation of the zerovalent NPs to ionic
silver (Ag+) in the aqueous solution10. The resulting change in the antiviral activity
of the oxidized AgNPs led to the conclusion that AgNPs exert a greater inhibitory
effect against SPV than ionic silver10.
Despite the growing amount of research with noble metal NPs and
various virus strains, a specific viral inhibition mechanism has not been
determined yet. Most literature studies strongly suggested that the primary
inhibition is a result of the NP disruption of the binding, fusion and/or entry steps
of the viral replication cycle4,8-9,22-25. The HIV-1 study showed that the AgNPs
bound to the GP-120 glycoprotein knobs of the virus through Ag-S interactions.
6
This is achieved through the reduction of disulfide bonds of the GP-120 proteins
that permanently alters the virus and its ability to bind with and infect the host
cell8,22. Similar inhibition mechanisms were proposed for the disruption of the
replication process of MPV4, tacaribe virus25 and HSV24. However, observation of
magnesium NPs within cultured cells suggested that the interruption of
intracellular biochemical pathways could also play a role in the inhibition
mechanisms of such NPs26.
Trefry (2011) examined the binding and fusion steps of the viral replication
cycle of VV that was exposed to NovaCentrix AgNPs (25 nm ± 10 nm in
diameter). The viral adsorption to host cells (i.e., binding) was found to be
unaffected by AgNP concentrations ranging from 2 – 128 μg mL-1. This
demonstrated that AgNPs did not alter the VV’s ability to bind to the host cell at
such concentrations5. Antiviral mechanisms associated with the entry/fusion
complex (EFC) were explored by examining the cytoprotective inhibition (cells
incubated with AgNPs pre-infection), the virucidal protection (VV incubated with
AgNPs prior to infection) and the post-infection inhibition (AgNPs applied as
treatment post-infection)5. The minimum inhibitory concentrations 50% (IC50) for
the three treatment conditions were found to be 91 μg mL-1, 48 μg mL-1 and 88
μg mL-1, respectively. This confirmed that VV’s fusion with the cell or entry into
the cell was disrupted by the AgNPs5. In addition, the virucidal condition of VV
inhibition was also investigated using Creighton AgNPs that had been filtered
using tangential flow ultrafiltration (TFU). These AgNPs were synthesized in our
7
group, and had a narrow size distribution of 1-20 nm with an average diameter of
11 nm27. The IC50 of these NPs was 128 μg mL-1. While a specific mechanism
was not proposed for the viral inhibition, the short incubation times employed in
this study (1 hr) and the observed antiviral activity of the two different types of
AgNPs strongly suggested that the inhibition occurred at the early stages (entry
and fusion) of the viral replication cycle5. Furthermore, the entry/fusion inhibition
scenario was supported with western blot testing using antibodies specific for the
G9 protein, a member of the EFC of VV. This analysis revealed a dose-
dependent viral inhibition and suggested some form of covalent interaction
between the AgNPs and the G9 proteins of VV5.
Several trends emerge from the above studies on various viral pathogens
exposed to various AgNPs. (1) The viral inhibition mechanism involves strong
interactions between the AgNPs and the virus that disrupt the early stages of the
viral replication cycle. Given the attraction of AgNPs to electron rich, organic
constituents, covalent bonding between the AgNPs and the amino acids (AAs) of
the viral proteins should be investigated. Additionally, the previous research on
VV supports the idea that the most probable interactions are occurring through
the protein moieties responsible for the entry and fusion steps (EFC proteins) of
the replication cycles. (2) Considering the enhanced antiviral activity that was
reported for unfunctionalized and functionalized AgNPs of an average diameter
of about 10 nm, and the high toxicity of AgNP capping agents or enclosing
matrices in host cells (e.g., foamy carbon matrix and PVP), unfunctionalized
8
AgNPs of a similar average diameter should be considered and tested as
effective antiviral agents.
The Creighton synthesis method is an inexpensive and simple procedure,
which results in the formation of unfunctionalized, round AgNPs of 1-100 nm in
diameter28-29. The polydispersity and concentration of the Creighton colloidal
AgNPs can be further controlled trough TFU. This thesis will show that TFU may
be implemented for the size-selection and extreme concentration of large
volumes of Creighton AgNPs. Afterward, these AgNPs of an average diameter of
11 nm of will be used in antiviral mechanism studies.30-31.
Surface-enhanced Raman spectroscopy (SERS) and scanning electron
microscopy – energy dispersive X-ray (SEM-EDX)
In this work, SERS and SEM-EDX measurements will be performed to
investigate the antiviral mechanism of unfunctionalized, Creighton AgNPs with
sizes in the 1-25 nm range and an average diameter of 11 nm.
Surface-enhanced Raman spectroscopy (SERS) is an effective method for
the detection of covalent bonding interactions between AgNPs and electron-rich
functional groups of proteins such as carboxylic, amide, thiol and aromatic AA
groups 32-33. Previous research indicated that AgNPs have preferential binding to
EFC proteins. The following interaction trend with AgNPs emerged from these
9
studies: carboxylic groups > peptide bond interactions (amide peaks) > aromatic
AAs > thiol groups > small side chains32-34. Given the complexity of the protein
structure EFC of the VV11, 35, numerous interactions with the unfunctionalized,
Creighton AgNPs would be expected. In this work, hyperspectral collection
methods that combine movable microscope stages with Raman instrumentation
will be employed to obtain a large, representative set of SERS spectra.
Furthermore, multivariate analysis techniques, specifically principal component
analysis (PCA) and vector component analysis (VCA), will be utilized to
mathematically identify the representative spectral patterns within the complex
hyperspectral dataset and to provide a statistical correlation of the representative
spectra to the compiled dataset36-39.
Finally, scanning electron microscopy – energy dispersive X-ray (SEM-
EDX) will be employed to confirm the direct, strong interactions between the
AgNPs and VV. SEM electron backscattering will be exploited to differentiate
between the organic constituents of the VV and any covalently attached AgNPs.
The contrast of backscattered electron images is directly related to the atomic
number of the elements in the sample8. More intense, brighter areas of the
image will correspond to heavier atoms (i.e., silver), whereas lighter elements
(i.e., organic constituents) will appear as darker areas40. Furthermore, EDX will
facilitate the elemental composition analysis of the VV samples treated with
AgNPs.
10
Hypothesis
Unfunctionalized, Creighton AgNPs of an average diameter of 11 nm will
covalently bind to VV mainly through the external, entry fusion complex (EFC)
proteins.
Specific aims
1) To fabricate, characterize and manipulate Creighton AgNPs of an average
diameter of 11 nm through UV-Vis absorption spectrophotometry,
inductively coupled plasma optical emission spectroscopy (ICP-OES),
micro-Raman spectroscopy, transmission electron microscopy (TEM),
scanning electron microscopy – energy dispersive X-Ray (SEM-EDX), and
tangential flow ultrafiltration.
2) To demonstrate the proposed antiviral mechanism through micro-Raman
spectroscopy, surface-enhanced Raman spectroscopy (SERS) and
scanning electron microscopy – energy dispersive X-ray (SEM-EDX).
11
Technical Approach
Specific Aim 1:
Reagent solutions and glassware preparation
Silver nitrate (AgNO3) and sodium borohydride (NaBH4) were obtained
through Fisher Scientific Inc. All solutions were prepared in highly purified water
(>17 MΩ) that had been autoclaved at 121°C for 20 min using a liquid
sterilization cycle. All glassware used for synthesis was cleaned by submersion
in an acid bath (10 % HNO3 bath for 12-24 hours) that was followed by a base
bath (1.25 M NaOH in a 40% ethanol base bath for 4-12 hours). Glassware was
rinsed three times with deionized water and one time with highly purified water
after each cleaning step. Afterward, the glassware was sterilized in an autoclave
at 121°C on a 20 minute gravity cycle.
Creighton synthesis of silver nanoparticles
A modified Creighton method28, 31 was utilized to synthesize the AgNP
colloid through the reduction of AgNO3 with NaBH4. A 2 mM solution of NaBH4
was prepared by dissolving 0.0227 0.0012 g of NaBH4 in 300 mL of autoclaved
water. A 1 mM solution of AgNO3 was prepared by dissolving 0.0085 ± 0.0009 g
AgNO3 in 50 mL autoclaved water. The reaction was carried out in an ice bath at
12
2 ± 1oC by adding AgNO3 drop wise to the NaBH4 at a rate of 1-2 drops/sec while
stirring at a rate of 325 rpm. To ensure the complete reduction of AgNO3, the
colloidal solution was stirred for additional 50 minutes within the ice bath. The
final colloid was refrigerated for a minimum of four days prior to characterization
by UV-visible absorption spectrophotometry and micro-Raman spectroscopy.
Colloidal batches of AgNPs, which passed the above nanomaterial
characterization tests, were combined. Figure 1 illustrates the synthesis setup
and the final color of the colloidal suspension of AgNPs. This Creighton colloid
was found to be stable for up to six months when stored at 4oC.
Figure 1: A) Creighton colloid synthesis setup and B) Characteristic golden-yellow color of the Creighton colloidal AgNPs30.
13
Characterization of silver nanoparticles
UV-Vis Absorption Spectrophotometry
Working Principle:
When subjected to electromagnetic excitation, some organic and
inorganics molecules will absorb radiation characteristic to the ultra-violet (UV) or
visible (VIS) range of the electromagnetic spectrum (approximately 200-800 nm).
Molecules that absorb UV-VIS radiation, commonly known as chromophores,
must contain electrons that are capable of undergoing the allowed electronic
transitions corresponding to this absorption41. Briefly, the molecules must
possess an electron in an -orbital or a non-bonded electron contained in a n-
energy level that can be excited to an antibonding *-orbital and an antibonding
* or *- orbital, respectively (Figure 2)41.
Molecules that absorb radiation from the VIS portion of the
electromagnetic spectrum such as many transition metals will transmit a color
visible to the human eye that is complementary to the color of the radiation
actually absorbed by the molecular species41. Creighton colloidal AgNPs, which
emit a characteristic golden yellow color, actually absorb radiation in the violet
portion of the spectrum at approximately 400 nm. The intensity of the color that is
observed by the naked eye often reflects the intensity of the characteristic
radiation that is absorbed by the molecule41.
14
Figure 2: Allowed electronic transitions for UV-VIS absorption: A) to *, B) n to
* and C) n to *.
The absorbed radiation or absorbance (A) is expressed mathematically as
the logarithmic ratio of the incident radiation power (P0) to the power of the
radiation transmitted from the sample (P). This is equal to the negative log of the
transmittance (T)42 (eq 1).
(1)
Spectra are normally plotted as a function of the absorbed radiation (in arbitrary
units) versus the wavelength (in nm). Typical spectra contain one or more broad
absorption bands that span a range of wavelengths and exhibit a maximum
15
absorption wavelength (λmax). The broad appearance of the absorption bands are
a result of the simultaneous absorption of closely spaced vibrational and
rotational energy transitions that are not resolved into discrete peaks38.
The shape, symmetry and intensity of the absorption bands can be used
to qualitatively define the molecular species or quantitatively estimate the
concentration of the analyte38. The approximate concentration can be obtained
through the use of Beer’s law relating the absorbance (A) to the analyte
concentration in moles per liter (c), the path length (b) and the extinction
coefficient of the analyte (ε) by the following equation42
(2)
where in this study, ε = 1.9 x 104 dm3 mol-1 cm-1 (for Creighton AgNP clusters
with λmax = 400 nm)43 and b = 1 cm (for standard cuvettes).
Sample preparation:
A 1:10 by volume dilution was prepared by pipetting 0.25 mL of AgNP
colloid into a cuvette of 1 cm path length (Fischer Scientific, 4.5 mL capacity)
containing 2.5 mL of highly purified water. A separate cuvette was prepared with
highly purified water for a blank baseline correction. The resulting solution was
mixed thoroughly. The outside walls of both cuvettes were cleaned with a dry
Kimwipe prior to analysis.
16
Instrumental Analysis:
A Cary 50 UV-VIS-NIR spectrophotometer from Varian Inc. (Figure 3) set
to absorbance mode was utilized to obtain the baseline corrected absorption
spectrum of the AgNP samples. Sample scans were collected within the 200-800
nm spectral range using 1 nm scanning intervals and a fast scan rate of 4800 nm
min-1. Prior to sample analysis, a baseline correction was obtained from the
absorption spectrum of the high quality water. The resulting spectra were plotted
and analyzed using Origin 8 software.
Micro-Raman Spectroscopy
Micro-Raman spectroscopy was used to verify the purity of the Creighton
AgNP batches before tangential flow ultrafiltration (TFU). The working principal,
Figure 3: Cary 50 UV-VIS-NIR spectrophotometer from Varian Inc. used to measure the SPR peak of the AgNP colloid.
17
sample preparation and instrumental analysis parameters are outlined under
Specific Aim #2.
Tangential Flow Ultrafiltration (TFU)
Working Principle:
Tangential flow ultrafiltration (TFU) is a recirculating filtration technique
that is commonly employed for the weight-based isolation of biological materials
such as proteins, cells and viruses. In this work, it has been adapted for the
effective size selection and concentration of AgNPs30-31.
Traditional dead-end filtration techniques such as centrifugation result in
nanoparticle aggregation. To overcome this limitation, synthesis methods often
employ chemically aggressive capping agents or solvent systems to achieve
stability upon nanomaterial concentration or specific size-selection44. The results
of this study will show that TFU (Figure 18) results in stable, homogenous
concentrates and eliminate the need of capping agents or solvent systems.
Briefly, the liquid sample (e.g., colloidal suspensions of nanoparticles) is passed
through a series of hollow fiber membranes of various pore sizes (from 1,000-kD
down to 10-kD) and surfaces areas (from 370 cm2 down to 5 cm2) to achieve the
desired size selection and level of concentration, respectively. In TFU, the
retentate feed is parallel to the membrane filter in comparison to most filtration
methods that utilize a perpendicular flow. Larger particles are retained by the
18
specific size lumen of the filter (retentate), whereas particles smaller than the
filter pore size will pass through (filtrate) (Figure 4).
Figure 4: TFU illustration of the selection process through the hollow fiber membrane. (Used with permission from Joshua D. Baker)
This parallel circulation helps prevent membrane clogging and
aggregation events that are common in traditional methods. As the retentate
continually feeds through the hollow fiber membrane, increasing amounts of
excess water and synthesis byproducts are removed through the passing filtrate,
while the AgNPs are retained with the recirculating retentate resulting in
increased concentrations of AgNPs in the final colloidal suspension (Figure 5)
19
Figure 5: Schematic illustrating the TFU working principle: a-c represent sucessive recirculating passes through the TFU system.
Sample preparation:
Each large batch of Creighton colloid (4.0 to 5.0 L) was processed through
the TFU system without the need of additional sample preparation.
Instrumental Analysis:
A KrosFlo II Research filtering system (Spectrum Laboratories, Rancho
Dominguez, CA) was used to size select and concentrate 4.0-5.0 L of AgNP
colloid (Figure 6).
a b c
1.
Feed Feed Feed
Filtrate Filtrate Filtrate
Retentate Retentate Retentate
20
Figure 6: KrosFlo II TFU system used for size selection and concentration of the AgNP colloid.
The TFU process was completed in a series of three steps30. In the first
step, AgNPs and AgNP-aggregates of 50-nm in diameter and larger were
eliminated using a 50-nm MidiKros® polysulfone module (460 cm2). Secondly, a
100-kD MidiKros® polysulfone filter (200 cm2) was employed to further size select
and concentrate the AgNPs contained in the filtrate collected from the 50-nm
filter. Size 17 masterflex tubing was used with a pump rate of 700 mL min-1 for
steps one and two. Finally, the concentrate retained from step 2 underwent a
further volume reduction using a 100-kD MicroKros® polysulfone filter (20 cm2)
connected with size 14 masterflex tubing and a pump rate of 90 mL min-1. Both
steps 2 and 3 eliminate AgNPs less than 1 nm in diameter and result in
approximate volume reductions of 100 fold. An original colloid batch of 5 L would
be reduced to approximately 50 mL in step 2 and then to approximately 4 mL in
step 3.
21
Inductively Coupled Plasma - Optical Emission Spectroscopy (ICP-OES)
Working principle:
When at room temperature, the electrons in an atom will occupy the
ground state. Upon exposure to flame or ionized plasma, these electrons will be
excited to an energetic state. The lifetime of these excitations are normally very
short (~10-7 – 10-9 s) and result in the emission of a photon of a wavelength (λ)
and an energy corresponding to the transition between the two energy levels42.
The characteristic excitation and emission process is depicted in Figure 7.
Figure 7: Excitation and emission process. A - D correspond various levels of excitation energy: A and B) Atomic excitation; C) Ionization and D) Ionization and excitation. λ1 – λ4 represent characteristic emission patterns: λ1, λ2 and λ3) Atomic emissions and λ4) Ionic emission.41
Ground State
Excited States
Ion Ground State
Ion Excited State
Excitation and Emission Process
E A B C D λ
1 λ
2
λ3
λ4
22
In ICP-OES, the excitation source is a plasma created by the inductively-
coupled ionization of a flowing argon stream42. The temperature range of the
resulting ionized plasma spans from 6,500 to 10,000 K, and creates an
environment capable of exciting most elements on multiple atomization or
ionization levels41. As a result, a complex emission spectrum is obtained for each
element present. Within the emission spectrum, intense spectral lines will be
present and will occur even for very low analyte concentration41. The
wavelengths of these emission lines are normally chosen for the sample analysis
and usually correspond to the electronic transition from an excited state to the
ground state (λ1 or λ2) according to the Boltzmann Distribution Law41. ICP-OES
offers many advantages over other atomic emission techiniques such flame
atomic absorption spectroscopy. A few examples include the simultaneous
multiple element analysis, the low detection limits (down to the g L-1 level) and
the elimination of many spectral and chemical interferences42.
Sample preparation:
Aliquot samples collected from the original Creighton colloid and the two
100-kD colloidal retentates were chemically digested using HNO3. Each colloidal
sample was volumetrically pipetted into a 50 mL beaker containing approximately
5 mL of highly purified water. One milliliter of trace metal grade HNO3 (Optima
grade, Fisher Scientific) was then added. The solution was gently heated at
225oC on a hotplate and allowed to gradually evaporate until nearly dry. The
23
samples were then volumetrically diluted in a 2% HNO3 matrix. The dilution
factors used in this study were 1:1,000 for the original colloid, 1:25,000 for the
first 100-kD MidiKros® retentate (200 cm2 module), and 1:250,000 for the second
100-kD MicroKros® retentate (20 cm2 module).
The following standards were prepared from a 10,000 g mL-1 (ppm) silver
standard solution for trace metal grade analysis (Ultra Scientific) in a 2% HNO3
matrix: 3, 7, 10, 15, 25, 50, and 100 μg L-1.
Instrumental analysis:
The amount of silver present in each colloidal sample was
quantified using a 710E spectrometer (Varian Inc.) (Figure 8). The ICP-OES
instrument parameters were as follows: wavelength for Ag (328.068 nm), radio
frequency power (1.20 kW), plasma flow (15.0 L min-1), auxiliary flow
(1.50 L min-1), and nebulizer pressure (200 kPa). Each sample was measured in
triplicate with a replicate read time of 10 s, a between-measurement stabilization
time of 15 s and a sample uptake delay of 30 s. Method blanks were introduced
between every sample to reduce potential contamination and a standard curve
was run every 10 samples to verify the stability of the emission intensity. The
resulting spectra were plotted using Origin 8 software and data was analyzed
using Microsoft Excel.
24
Scanning Electron Microscopy – Energy-Dispersive X-Ray (SEM-EDX) and
Transmission Electron Microscopy (TEM)
Working Principle:
Electron probe characterization methods (e.g., SEM-EDX or TEM) are
important tools for imaging, chemical composition determination and surface
analysis of nanomaterials40. In these microscopy techniques, an electron beam
referred to as the primary (10) probe projects electrons onto the surface of the
sample and elicits a variety of scattering effects. The scattering is highly
dependent on the mean free path of the electron, the scattering cross-section
and the composition and thickness of the sample40.
Figure 8: 710E ICP-OES (Varian Inc.) used to quantify the amount of silver present in each colloidal sample.
25
SEM-EDX is typically used for thick sample materials when compared to
TEM. SEM-EDX measures the electron-sample interactions that occur in an
interaction volume typically of 1 – 5 m in depth, and is dependent upon the
density of the sample and the electron energy40. While several electron
interactions are possible, SEM primarily measures secondary (20) and
backscattered electrons. When coupled with EDX, characteristic X-rays that are
emitted from electron cascade events can also be measured40.
Low energy 20 electrons, excited from the K-shell of the atom, are
produced when 10 electrons release sufficient energy at the atom’s core to
overcome the work function of the material40. The SEM image formed from the
collection of the 20 electrons can be used to elucidate the morphology or
topology of the sample surface or the shape of particles present in the sample40.
Additionally, the vacancy left behind by the excited 20 K-shell electron is
subsequently filled when an electron from a higher energy level cascades down
to fill the vacancy. The X-rays emitted by the cascading electrons may be
differentiated using an EDX detector to provide a unique spectral fingerprint for
the atoms present40. The backscattered electrons are produced by 10 electrons in
a lower yield than the 20 electrons. These backscattered electrons are deflected
backwards from the surface and depend on the atomic species and the volume
of excitation40. The elemental composition of the sample can be determined
through topographical images generated from backscattered electrons with
brighter areas corresponding to elements of high atomic numbers40.
26
Comparatively, TEM is an effective technique for the analysis of thin
samples (100 nm). TEM is commonly used for estimating the size, shape and
size distribution of nanomaterials. In TEM, there is no appreciable electron-
sample interaction as the electrons transmit through the sample. The amount of
electrons transmitted is dependent upon the atomic or molecular weight of the
sample40. Lighter weight atoms typically transmit more electrons than heavier
atoms, which exert more influence on the transmitting electrons40. As a result,
light areas correspond to lower weight species and darker, more electron-dense
areas are occupied by heavier atoms in the resulting bright field image on the
phosphor screen40.
SEM-EDX Sample Preparation:
Three samples were prepared for SEM-EDX analysis: 1) the final 100-kD
retentate of AgNPs (8538.9 mg mL-1), 2) vaccinia virus (1012 PFU) and 3)
vaccinia virus that was incubated with 1000 mg mL-1 of AgNPs for 1 hour at
370C, washed and filtered through a 100 nm filter to remove any unbound
AgNPs. Both virus samples were deactivated with formaldehyde prior to
application. A small volume of each sample was applied to a stub and allowed to
dry.
27
SEM-EDX Instrumental Analysis:
A Jeol JIB 4500 SEM (Figure 9) equipped with an EDX detector was
utilized for data collection.
Figure 9: Jeol JIB 4500 scanning electron microscope used for SEM-EDX measurements.
TEM Sample Preparation:
The original Creighton AgNPs and the final 100-kD retentate of AgNPs
were diluted in highly purified water as described in reference 30. Twenty
microliters of each sample were deposited onto 300-mesh formvar-coated gold
grids (Electron Microscopy Sciences) and allowed to dry in a desiccator before
viewing within one day.
28
TEM Instrumental Analysis:
A Phillips EM 208S transmission electron microscope operating at an
accelerating potential of 70 kV was used to visualize the nanoparticle samples as
described in reference 30. A high resolution Gatan Bioscan camera was used to
capture the electron micrographs. ImageJ45 software was used to analyze the
tagged image files (TIF). One AgNP was defined by a complete and enclosed
perimeter. Two hundred particles per sample were analyzed. Origin 8 software
was employed to construct a TEM size histogram for the diameters of the
selected AgNPs.
Specific Aim 2:
Virus Sample Preparation
A virus stock was prepared and donated by Dr. John Trefry in Dr. Dawn
Wooley’s laboratory.
Micro-Raman and Surface-enhanced Raman Spectroscopy (SERS)
Working Principle:
Theory shows that the Raman effect is an inelastic backscattering process
that results when molecules in a sample are excited by a monochromatic laser
29
beam46. When an incident photon from the laser beam collides with the molecule,
the electron cloud becomes polarized and the electrons are excited to a virtual
energy state raising the potential energy of the molecule ( ) above the ground
state (Figure 10).
Figure 10: Virtual energy state attained by excited electrons
The majority of the excited molecules will relax immediately back down to
the initial ground state by emitting a photon of energy identical to the incident
photon. This phenomena is commonly known as Rayleigh scattering or elastic
scattering (Figure 11b)47. Approximately 1 in 106 molecules experience inelastic
scattering or Raman scattering, and emit a photon of energy that is either greater
than or less than the energy of the incident photon. Stokes scattering (Figure
10a) occurs when the final vibrational state is more energetic than the initial state
and the photon is shifted to a lower frequency. This Stokes radiation has less
energy than the incident photon ( )47. The anti-stokes scattering
𝑣 of the laser
30
(Figure 11c) occurs when the final vibrational state is less energetic than the
initial state. The anti-Stokes radiation has a higher frequency and more energy
than the incident photon ( )47.
Figure 11: Fundamental principle of Raman spectroscopy: a) Stokes scattering, b) Rayleigh scattering and c) Anti-Stokes scattering.48
The inelastic backscattering modes are symmetric relative to the Rayleigh
peak with Stokes peaks and anti-Stokes peaks occurring at lower and higher
wavenumbers than the Rayleigh scattering, respectively. Stokes peaks are
generally higher in intensity and most often chosen for analysis since the
energy level is more populated than the higher energy levels of the anti-Stokes
scattering according to the Boltzmann Distribution Law42.
Raman spectroscopy offers several advantages as an analytical tool.
Samples in various physical states can be analyzed including liquids, solids and
gases with minimal sample preparation. Raman spectroscopy is also a non-
invasive technique which facilitates the sample recovery for further analysis by
hν0
hνS
hνf hν
f
f
i
R
Stokes Rayleigh Anti-Stokes
hνAS
ground state
virtual state
excited state
hν0
hν0
a b c
hν0
31
other methods. Additionally, Raman spectroscopy provides a unique molecular
fingerprint and in general sharp Raman bands that enable multiplex detection. Its
selection rules make this technique complimentary to infrared (IR) spectroscopy.
Finally, a large range of wavenumbers (100 – 4000 cm-1) can be analyzed to
obtain the entire spectral region of interest without changing filters, gratings or
detectors.
One inherent disadvantage is that low signal intensity may occur for dilute
or lowly concentrated samples due to the extremely low number of photons that
experience inelastic backscattering (approximately one in a million).
Fluorescence interferences may also result especially with higher energy
excitation sources. Furthermore, sensitive biological samples may experience
undesired photodecomposition depending on the acquisition parameters chosen
for analysis. While inconvenient, most of these limitations can be managed by
employing surface-enhanced Raman spectroscopy (SERS).
The SERS effect was discovered in 1974, when Fleischmann and
coworkers noticed an enhancement in the signal of the Raman spectra of
pyridine located in the close proximity to a silver electrode49. This enhancement,
initially thought to be due to presence of a larger number of pyridine molecules
adsorbed onto the nano-roughened silver electrode49. Later, it was shown that
this enhancement (up to 106) was much too large to be accounted for by the
increase in surface area of the silver electrode alone. It was attributed to a
32
combination of electromagnetic and charge transfer enhancement
mechanisms50.
The electromagnetic enhancement mechanism is a result of the
resonance surface plasmons of noble metal nanomaterials such silver, gold and
copper nanoparticles and nanofilms. Silver nanoparticles have unique
electromagnetic properties that emerge from their lone s electrons (i.e., the so
called surface plasmons). When the noble nanoparticles are exposed to an
electrical field at the frequency of the laser beam (Eincident), the particles become
polarized in the direction of the electrical field and subsequently produce an
electrical field (Einside) that contributes additively to the incident electrical field
(Etotal) (Figure 12).
Figure 12: Schematic of the electromagnetic enhancement mechanism in SERS.
Free electron
metal particle
𝑬 𝒊𝒏𝒄𝒊𝒅𝒆𝒏𝒕
𝑬 𝒊𝒏𝒄𝒊𝒅𝒆𝒏𝒕
𝑬 𝒊𝒏𝒔𝒊𝒅𝒆 𝑬 𝑻𝒐𝒕𝒂𝒍 𝑬 𝒊𝒏𝒄𝒊𝒅𝒆𝒏𝒕+ 𝑬 𝒊𝒏𝒔𝒊𝒅𝒆
Before Interaction
𝑬 𝒊𝒏𝒄𝒊𝒅𝒆𝒏𝒕
After Interaction with laser light
AgNP
33
The presence of a molecule in close proximity to the nanomaterial surface
will likewise create a resonance effect51. This electromagnetic mechanism
accounts for a total enhancement of the Raman signal of up to 105-106, whereas
the chemical adsorption mechanism or charge transfer theory will contribute a
maximum of 103 to the signal enhancement51. In 1999, Xu and coworkers
demonstrated that the total SERS enhancement factor may reach values as high
as 109-1011 for analyte molecules located in the interstitial space between two
nanoparticles. This SERS enhancement is highly dependent on the distance
between the two nanoparticles and the laser polarization with respect to the
nano-dimer axis52. The highest SERS enhancements were later observed
experimentally for small junctions of linked AgNPs (< 5 nm) with the analyte
molecule located in this hot spot and a polarization along the AgNP-dimer axis51-
53.
Sample preparation:
AgNP Colloid: AgNP colloid was transferred into a clean 2 mL quartz cuvette
with plastic plug. A Kim wipe was used to clean off finger prints, smudges or
colloid from the surface of the cuvette prior to placing the cuvette on the
microscope stage.
34
Viral samples and controls: Three sample spots were applied to an autoclaved
glass microscope slide for micro-Raman analysis: 1) 100-kD colloidal AgNP
suspension (1000 mg mL-1; AgNP alone), 2) vaccinia virus (1012 PFU; VV alone)
and 3) vaccinia virus that was incubated with 1000 mg mL-1 of AgNP for 1 hour at
370C, washed and filtered through a 100 nm filter to remove any unbound AgNP
(AgNPs & VV). Both virus samples were deactivated with formaldehyde prior to
application. Figure 13 illustrates the sample slid configuration.
Figure 13: Microscope slide preparation for virus samples.
Instrumental analysis:
A LabRamHR 800 Raman system (Horiba Jobin Yvon, Inc.) (Figure 14)
equipped an Olympus BX41 confocal Raman microscope) and a holographic
grating of 600 grooves/mm was employed for the both the AgNP colloid purity
analysis and virus sample analysis.
AgNPs & VV
VV alone
AgNPs alone
35
Parameters for AgNP colloid purity: Laser focusing of the AgNP colloid was
conducted in video mode using the 50x objective lens. Directly observation of the
focal point and confocal cone of the laser in the colloidal suspension facilitated
the focusing procedure. A He-Ne laser (632.8 nm) with approximately 17 mW of
laser power at the sample was employed as the excitation source. The confocal
hole was set at 300 m. Micro-Raman spectra were collected over a 100 – 4000
cm-1 acquisition window using the 50x long working distance air objective lens.
An exposure time of 30 s with 5 accumulation cycles was utilized. A CCD
detector (1024 x 526 pixels) with a spectral resolution of 1 cm-1 was employed for
spectral data collection. The micro-Raman spectra were plotted and analyzed
using Origin 8 software.
Parameters for Virus Sample Analysis: Laser focusing of the virus samples
and controls were conducted in video mode using the 100x objective lens. A He-
Ne laser (632.8 nm) was employed as the excitation source with a confocal hole
set at 300 m. A D1 filter was utilized to attenuate the laser power by a factor of
10 resulting approximately 1.7 mW of laser power at the sample. Micro-Raman
maps using a 1 m step size were collected over a 100 – 2000 cm-1 acquisition
window using the 100x objective lens. An exposure time of 4 s with 3
accumulation cycles was utilized. All multivariate analysis techniques were
executed using MatLab v. 7.11.0.584 (R2010b) with in-house written codes.
Origin 8 software was used to plot all represented spectra.
36
Figure 14: LabRamHR 800 Raman system (Horiba Jobin Yvon, Inc.) used to verify AgNP colloid purity and analyze virus samples.
Hyperspectroscopy and Multivariate Analysis Methods
Working principle:
Given the complex and heterogeneous morphology of biological samples,
advanced spectroscopic techniques may be needed to accurately access the
molecular fingerprint of the sample. For example, hyperspectroscopy combines
vibrational spectroscopy techniques such as Raman spectroscopy with movable
microscope stages to facilitate the evaluation of the molecular composition over a
selected, 2D space of the sample54-55. Traditional vibrational spectroscopy
typically allows for the fast acquisition of point spectra from a specific location on
the sample and requires a minimal analysis time. The data obtained in the form
of a 2D plot is easily evaluated with the help of univariate analysis techniques
and provides information about molecular composition, structure and interactions
37
within a sample42. Given the simplicity of the analysis, vibrational spectroscopy is
a preferred analysis method for homogeneous samples in many scientific
disciplines such as chemistry and biology. In contrast, hyperspectroscopy allows
for the collection of multiple sample measurements using mapping techniques
with a movable microscope stage on which the sample is placed37,55-56. The
resulting data is compiled into a 3D hyperspectral dataset54,39 that can be
examined using both univariate or multivariate analysis techniques. This provides
information about the molecular composition and morphology of the sample37,54-
55. The wealth of information that can be gleaned from this type of analysis
makes hyperspectroscopy very suitable for heterogeneous samples. However,
extensive acquisition and analysis time may be required37.
Hyperspectral datasets are 3D matrices consisting of a 2D spatial
mapping surface, which is typically represented with x and y coordinates, and a
series of wavelengths constituting the third dimension called spectral axis54
(Figure 15).
38
Figure 15: Hyperspectral dataset representations. The images in the left and right columns represent the spatial orientation of the data selection and the corresponding spectral image taken from hyperspectral data obtained from micro-Raman analysis of the cross section of chick embryo bones. A) Spatial selection is equivalent to point spectrum measurements; B) Several point spectrum measurements corresponding spatially to a data obtained from a linear section across the sample surface; C) An RGB image obtained from the analysis of the signal response obtained at that specific wavenumber from the spectral axis for all 2D points on the sample surface.38.
The representative 2D data image or spectra obtained is determined by
the location of the selected data within the 3D hyperspectral array38. If a single
a
b
c
Y
X
X
Y
X
λ
Y
λ
λ
39
spectrum was desired (Figure 15a), the spatial selection would be equivalent to a
point measurement in vibrational spectroscopy. Evaluating a series of points
scanned on the surface (a line in the 2D plane) across the entire spectral axis
would yield multiple point spectra within the same plot (Figure 15b). Lastly, a
color map (Figure 15c) can be created using RGB imaging techniques to indicate
“hot spots” or points of high intensity for a specific vibrational mode38. This can
be achieved by analyzing the signal response obtained at that specific
wavenumber from the spectral axis for all 2D points on the sample surface.
Given the complex nature of a hyperspectral dataset, well-developed data
analysis protocols are recommended. The first step in the process is the
elimination of any anomalous cosmic ray features or spikes followed by the
application of a baseline correction or normalization algorithm for the reduction or
elimination of noise related spectral components and fluorescence38. After
preprocessing, a reduction of the data set may be needed to eliminate spectra
attributed to noise related features or non-sample spectra. Principal component
analysis (PCA) is a data reduction algorithm that is commonly employed in such
situations (to be discussed later)38. The actual analysis of the hyperspectral
dataset depends on the desired result. Univariate analysis techniques such as
single peak area integration offer relatively rapid analysis of the complex
dataset55. However, more complex algorithms termed multivariate analysis
methods are needed if a comprehensive look at the morphological characteristics
of the sample is desired54,38.
40
The underlying premise of multivariate analysis techniques is that the
hyperspectral dataset will contain a fixed number of spectral features called
“endmembers”. These endmembers will account for the majority of the spectral
variance within the data set36,56. The multivariate algorithms determine the
fraction of the data set that each endmember accounts for, the so called
fractional abundances36. When these fractional abundances account for most of
the variance of the spectral data set (usually 95%) then the entire spectral data
set can be represented by these endmember spectra or a linear combination of
these spectra and noise related features54,36. The noise contribution to the
cumulative variance of the dataset is in general smaller than 1%36,54. A good
example of endmember representation is provided by De Juan et.al (2004)37.
Briefly, they performed an analysis of five pharmaceutical blend samples with
known, varying amounts of an excipient ingredient and an active pharmaceutical
ingredient for proof of concept (API). The first sample was prepared with 100%
excipient and no API, the next four samples contained 80% excipient, 20% API;
60% excipient, 40% API; 40% excipient, 60% API and finally 20% excipient, 80%
API37. The PCA multivariate analysis method was used to extract the
representative endmembers, termed principal components (PC), from the
hyperspectral datasets of each sample (Table 1).
41
Table 1: Percent cumulative variance determined by increasing PCA model size37.
For the first sample, 96.33% of the variance of the dataset was accounted for
with one PC or the excipient. As previously stated, all the remaining PCs added
less than 1% to the cumulative variance and therefore are considered noise-
related features37. Two PCs were necessary for the second sample analysis,
namely excipient and API and accounted for 95.26% of the variance. With
samples three through five, a third PC was needed to account for the majority of
the variance, which the authors attributed to an impurity component37. The
identified PC(s) correlated very well with the composition of the five samples.
Overall, this study demonstrates that PCA could be successfully used for the
identification of spectral features of individual components of a complex sample.
The PCA technique is a relatively popular multivariate method because of
its mathematical simplicity. PCA is an unsupervised method that identifies
spectral patterns or similarities within a dataset called PCs37. PCA works well in
differentiating noise related spectra and can help reduce the dimensionality of the
42
dataset if needed even when a different multivariate technique is ultimately
desired for the analysis54. As a principal analysis technique, PCA works best with
data that has some degree of reproducibility. Mathematically, the PCs are
eigenvectors calculated from the covariance of the data matrix. The first PC is
the eigenvector with the highest eigenvalue and will explain the highest
percentage of variance in the dataset37. The second PC accounts for the next
highest percentage of the residual variance (the eigenvector with the second
highest eigenvalue) and is orthogonal to the first eigenvector and so forth (Figure
16)37.
Figure 16: Geometric representation of PCs54.
Finally, all spectra in the original data set are projected onto the PC basis set and
expressed as linear combinations of the PCs. The individual spectra are then
z
x
y
Principal Component 2
Principal Component 1
43
given a “score” indicating how well the given spectrum correlates with the PC54.
These score projections are useful for grouping spectra according to their
correlation to the PCs and provide a useful tool for differentiating samples based
on their spectral similarities57.
Another common multivariate technique used for unsupervised datasets is
vector component analysis (VCA). Like PCA, VCA reduces the data set to a finite
number of endmembers that are a linear representation of the dataset56. Unlike
PCA, VCA selects endmembers that are “pure component spectra” and are
considered outliers of the hyperspectral dataset56. Each pure component
spectrum is assigned a different vector 56. The combination of these vectors
forms a simplex with the most extreme spectra defining the edges of the simplex
boundaries56. Each spectrum within the dataset is then defined as an orthogonal
projection onto the set of endmembers56. Since the mathematical basis of VCA
focuses on spectral differences, the representative set of VCA endmembers
should define the most significant spectral features of the hyperspectral
dataset39. This makes VCA a useful multivariate technique for complex biological
samples, which in general exhibit a large number of unique spectral differences
within the spectral dataset.
Miljkovic et.al (2010) applied the VCA algorithm to several HeLa cell
hyperspectral Raman datasets and imaged the cells using a finite number of
endmembers to obtain an RGB image of the sampled cells. The algorithm was
able to identify unique biological components of the cells including mitochondrial
44
tubules, nuclei, nucleoli, phospholipid inclusions, and the cytoplasm39. These
label-free Raman images (see reference 39) demonstrated a high degree of
correlation when compared to the images produced by traditional fluorescent
imaging techniques. Given that the VCA images displayed several cellular
components at the same time, a huge advantage was achieved over the
fluorescence techniques, which are normally constrained to one biological
component. Considering the biochemically diverse nature of different cellular
components, this multivariate method proved successful in isolating unique
spectral characteristics of the cellular matrix39.
Instrumental Analysis:
Pre-processing
Combined representative hyperspectral data sets were created in MatLab
v. 7.11.0.584 (R2010b) for the VV-AgNP and control samples (AgNP alone and
VV alone). Prior to the application of any multivariate algorithms, all spectra
within the dataset were normalized according to the minimum and maximum
intensity values present in the individual spectra. First, the lowest negative
intensity value of the spectrum was subtracted from all points within the spectrum
to normalize the baseline to zero. If no negative values were observed within the
spectrum, the step was not performed. Next, all points within the spectrum were
divided by the maximum intensity value within the spectrum to create a spectral
45
dataset where all spectral points lied between zero and one. This process
corrected for baseline anomalies or spectral artifacts.
For the VV-AgNP sample, a signal-to-noise threshold algorithm was used
to select representative spectra from the dataset that exhibited 30% or greater of
the maximum baseline corrected relative peak intensities. The following selected
bands of interest were used for this screening process: 450-550 cm-1, 550-650
cm-1, 650-750 cm-1, 750-850 cm-1, 850-950 cm-1, 950-1050 cm-1, 1000-1100 cm-1,
1100-1200 cm-1, 1200-1300 cm-1, 1300-1400 cm-1, 1400-1500 cm-1, 1600-1700
cm-1 and 1800-1900 cm-1. Most of the spectral features characteristic to the VV-
AgNP interaction fell within the above spectral ranges. After selection, each
spectrum was eliminated from the larger hyperspectral data set to avoid the
selection of duplicate spectra. Finally, the compiled selected spectra were
examined individually to eliminate any anomalous spectra that were not
previously discarded in the preprocessing protocol.
Multivariate Data Analysis:
Vaccinia virus control: No multivariate data analysis techniques were applied to
the vaccinia virus control dataset.
AgNP control: PCA was applied to the normalized hyperspectral dataset. Any
PC that contributed more than 1% to the cumulative variance of the dataset was
46
retained, while any PC that added less than 1% was considered a noise-related
component and was discarded.
Vaccinia virus-AgNP sample: VCA was utilized to select 50 representative
endmember (EM) SERS spectra. This number of EMs (N = 50) was chosen to
provide a large enough set of representative SERS spectra that would comprise
all unique spectral features. At the same time, this number of EMs would not
contain an overwhelming amount of repetitive SERS spectra. Origin 8 software
was used to plot all EM spectra and to assign the SERS peaks. A pairwise
correlation coefficient algorithm was then employed to quantify the spectral
profile and the degree of similarity in each of the 50 EMs with all other
representative endmembers. The correlation threshold values were 95%, 90%,
85%, 80% and 75%. After correlation, six representative SERS spectra were
selected by examining individually each of the 50 EMs. These six EMs accounted
for the most unique spectral features and corresponded to a threshold value of
85% (i.e., they demonstrated the lowest correlation in the pairwise comparison).
At higher correlation values (i.e., 90-95%), too few representative SERS spectra
were selected and some unique vibrational modes were lost, whereas at lower
correlation values (i.e., 75 – 80%), too many repetitive SERS spectra were
included. The correlation algorithm was then employed a second time to
determine the spectral similarity of these representative EMs to the entire
compiled hyperspectral dataset using the same correlation thresholds.
47
Results and Discussion
Specific Aim 1:
Synthesis of silver nanoparticles
Five liters of AgNP colloid were successfully synthesized using a modified
Creighton method29. For the reaction, 50 mL of AgNO3 (0.0516 g of AgNO3 in
300 mL of autoclaved water) was reacted with 300 mL of NaBH4 (0.0463 g of
NaBH4 in 600 mL autoclaved water). The resulting colloidal suspension
contained 5.06 x 10-5 moles of Ag+ equating to 5.46x10-3 g of Ag+ in a total
reaction volume of 350 mL. The theoretical yield was 15.6 g mL-1 of silver.
Characterization of silver nanoparticles
UV-Vis absorption spectrophotometry
UV-Vis absorption spectrophotometry was used to estimate the
approximate shape, average size, size distribution, aggregation state, and
concentration of the colloidal AgNPs. The estimates can be made using the
surface plasmon resonance peak of the colloidal AgNPs. AgNPs have special
optical properties that are associated with the lone s electrons (surface
plasmons) present in silver metal atoms29 of a [Kr]5s14d10 electron configuration.
When exposed to incident laser light, the surface plasmons are polarized by the
electromagnetic field of the laser light (i.e., the s electrons will move away from
48
the nucleus in the direction of the electrical field)29. Inevitably, the surface
plasmons will experience an electrostatic attractive force back towards the
nucleus of the silver atom. It is these collective oscillations of the electrons
(surface plasmon resonance) that gives rise to the characteristic yellow color of
the colloid and the surface plasmon resonance (SPR) maximum at approximately
400 nm29,58.
The approximate shape, average size and aggregation state of the
Creighton AgNPs can be verified by the number of absorption bands present and
the λmax of the SPR peaks. The typical UV-Vis absorption spectrum for single
element spherical nanostructures (i.e., having an aspect ratio of 1:1) exhibits one
SPR peak. Nanorods, which are anisotropic in shape have differing dimensions
for the transverse and longitudinal axes, will absorb UV-Vis radiation at two
separate λmax values. Mohapatra et.al (2010) demonstrated this principal with the
UV-Vis absorption spectrum of silver nanorods that contained two distinct peaks
at 420 and 582 nm corresponding to the transverse and longitudinal SPR bands,
respectively59. Furthermore, if the Creighton AgNPs form larger aggregates, the
SPR peak broadens and red-shifts to higher wavelengths. Likewise, a blue-shift
would be indicative of a NP suspension with particles of smaller sizes58. Mie
theory (eq. 3) was used to quantify this wavelength/particle size-dependence and
the average diameter of the Creighton AgNPs60
49
(3)
where λmax is the maximum wavelength value (λmax), ω represents the full width
at half maximum (FWHM) of the SPR peak, c refers to the velocity of light (2.998
x 108 m s-1), and Vf denotes the Fermi level electron velocity of silver (1.4 x 106 m
s-1)60. The estimated average particle size for the Creighton AgNP suspension
depicted above (λmax = 394 nm and a FWHM of 52.4 nm) was determined to be
4.4 nm. TEM analysis (discussed later) revealed the actual average particle size
for the AgNP colloid was 28.1 nm.
The SPR peak obtained for the Creighton AgNPs through UV-Vis
spectrophotometry is pictured in Figure 17. The sharp, symmetrical nature of this
peak with a λmax at 394 nm is indicative of small (average diameter of 10-20 nm),
spherical and moderately aggregated AgNPs.
Figure 17: UV-Vis absorption spectrum of the colloidal AgNPs30.
50
Using Lambert Beer’s law (eq. 2)
(2)
the average silver concentration of the colloid was estimated at 9.47 g mL-1
where ε = 1.9 x 104 dm3 mol-1 cm-1 (for AgNP clusters with a λmax = 400 nm)43, A
= 0.18 (Figure 17) and b = 1 cm. The actual silver yield obtained through ICP-
OES (discussed below) was 14.3 and 15.2 g mL-1 for two representative
batches. These values are more accurate (i.e., close to the theoretical yield of
15.6 g mL-1).
In general, the shape, average size, size distribution, aggregation state
and concentration of AgNPs obtained from the SPR absorption peak are
estimations at best and more sophisticated techniques such as TEM and ICP-
OES are necessary for the accurate determination of these parameters.
However, UV-VIS absorption spectrophotometry demonstrated to be a valuable,
inexpensive and time efficient screening tool that facilitated the selection of
“acceptable” colloidal batches of AgNPs for further quantification and use in
biological applications.
51
Tangential flow ultrafiltration (TFU)
The size selection and concentration of Creighton AgNPs was
accomplished in three steps. The schematic depicted in Figure 18 demonstrates
the steps involved in the TFU process, the filter modules employed and the
colloidal suspensions retained in each step of the process. In the first step, five
liters of colloid was filtered through a 50 nm MidiKros® polysulfone (200 cm2)
module resulting in a volume reduction of approximately 100 mL. In the second
step, the remaining 4.9 L of 50 nm filtrate was then processed using the 100-kD
MidiKros® filter (200 cm2). The retentate volume of 50 mL was saved and
resulted in a 98-fold volume reduction. In the last step, the retentate from step
two (100-kD retentate) was filtered through a 100-kD MicroKros® polysulfone
module (20 cm2). The final 100-kD retentate volume was 4 mL, which
corresponded to an additional 12.5-fold volume reduction.
Overall, a 1,225-fold volume reduction was achieved through the three-
step TFU process. Since the volume reductions are dependent upon the initial
volume of Creighton colloid and the surface area of the filtration modules, the
TFU process can be modified to obtain the desired concentration and volume of
final retentate of AgNPs. In this study, the two volume reduction steps resulted in
retentate volumes of approximately 50 mL (100-kD module of 200 cm2) and 4 mL
(100-kD module of 20 cm2), respectively. If a smaller volume of colloid is
processed, the initial volume reduction would be smaller (i.e., the initial volume
reduction would be approximately 60-fold for 3 L of colloid). Likewise, if filtration
52
modules with smaller surface areas are chosen, the retentate volumes would
decrease (i.e., larger volume reductions) and the final concentration of AgNPs
would increase.
Figure 18: Schematic illustrating the three-step TFU process. The green-shaded boxes indicate the AgNP colloidal samples retained for analysis. Vial photographs show A) Original colloid, B) 50 nm filtrate collected after processing the original colloid through the through the 50 nm filter, C) 100-kD retentate obtained after the first volume reduction using the 100-kD MidiKros filter (200 cm2) and D) 100-kD final retentate resulting from the second volume reduction using the 100-kD MicroKros filter (20 cm2)30.
A Original Colloid
50 nm Concentrate 50 nm Filtrate
100 kD Concentrate 100 kD Filtrate
50 nm MidiKros
100 kD MidiKros
filter
100 kD Concentrate 100 kD Filtrate
100 kD MicroKros filter
C
B
D
A
53
The drastic change in color from light yellow for the original Creighton
colloid to a very dark brown for the final 100-kD retentate (Figure 18) is a clear
indication of the large degree of concentration associated with the extreme
volume reduction (from 5 L down to 4 mL of colloidal suspension) of the three-
step TFU process.
Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES)
ICP-OES was used to quantify the silver amount in the original Creighton
AgNP colloid and the 100-kD final retentate. A linear calibration curve was
constructed from eight silver standards (0, 3, 7, 10, 15, 25, 50, and 100 μg L-1)
prepared in a 2% nitric acid (Figure 19). The correlation coefficient of the
calibration curve (R2 = 0.9999) validated the curve linearity over a wide range of
silver concentrations.
Figure 19: IPC-OES calibration curve prepared from eight Ag standards (0, 3, 7, 10, 15, 25, 50, and 100 μg L-1).
54
The silver amount in each of the digested colloidal suspensions (i.e.,
samples A, C and D from Figure 18) was extrapolated from the linear ICP-OES
calibration curve. Table 2 lists the silver amounts that were obtained for two
independent Creighton colloid batches.
Table 2: Silver amounts determined by ICP-OES for the representative TFU samples of two independent bacthes: A) Original colloid, B) Initial 100-kD retentate, and C) Final 100-kD retentate.
Batch 1 Volume of
Suspension (mL)
Silver amount
(g mL-1)
Original Colloid (A) 5,000 14.3
100-kD concentrate (B) 50 1,020
100-kD concentrate (C) 4 10,400
Batch 230
Original Colloid (A) 4,000 15.2
100-kD concentrate (B) 50 683.1
100-kD concentrate (C) 4 8,540
The percent yield for the Creighton reaction was estimated to be 91.6%
and 97.4% for the two colloid batches. The percent reaction yield was estimated
as the ratio of the actual yield to theoretical yield as indicated below:
55
(4)
In the first step of the TFU process, an original Creighton volume of 5 L
(14.3 g mL-1) was concentrated into 50.0 mL of colloidal suspension (1,020 g
mL-1). This corresponds to a concentration factor and a percent recovery of
approximately 71-fold and 71.0% recovery of AgNPs, respectively.
(5)
(6)
During the second step of the TFU process, 82.2% of the AgNPs were
retained with a concentration factor of approximately 10-fold.
(7)
(8)
For the entire TFU process, an approximate 730-fold concentration
resulted in a 58.2% recovery.
(9)
(10)
56
Similar results were observed for the total TFU process for batch two: an
approximate 560-fold concentration and a 56.2% recovery.
(11)
(12)
The remaining 40% of AgNPs that were not retained through the TFU
process could be attributed to larger AgNPs and AgNP-aggregates removed
through the 50 nm filtration step. AgNPs of smaller diameters that were not
recovered through the successive steps of the TFU process might also contribute
to this percent loss.
Overall, TFU proved to be a relatively rapid (6 hours) and inexpensive
concentration method for AgNPs (from 15.2 g mL-1 of silver in the original colloid
to 10,400 g mL-1 in the final 100-kD retentate). In this process, most of the
excess reagents and synthesis byproducts were eliminated together with the
water without the use of additional solvent. Therefore, TFU may be successfully
utilized for the “green” manipulation of nanoparticles in preparation for
nanotoxicity or SERS-based sensing studies.
57
Transmission electron microscopy (TEM)
The TEM images, size histograms and data presented in this section are
part of a study that has been accepted for publication in the Journal of Visualized
Experiments (JoVE)30. TEM micrographs and size histograms of the original
Creighton colloid and the final 100-kD retentate of AgNPs are depicted in Figure
20.
Figure 20: TEM micrographs and TEM size distribution histograms of original Creighton AgNP colloid (A and B) and final 100 kD AgNP colloidal suspension (C and D). The inset on the original colloid histogram (B) represents an expanded view of the 41-75 nm size range for comparison. The size bar is 100 nm for the TEM micrographs30.
58
Visual inspection of the TEM micrographs and TEM size histogram of the
original Creighton colloid revealed a polydispersed colloidal suspension
containing AgNPs and AgNP-aggregates larger than 50 nm in diameter
(approximately 1.0% of the total number of AgNPs) and a size distribution of 1-75
nm. In comparison, the final 100-kD retentate contained no AgNPs and AgNP-
aggregates of 50 nm and larger, and had a much narrower size distribution. It
consisted mostly of AgNPs of 1 - 20 nm in diameter having only 12.4% of the
AgNPs present in the 21-40 nm size range. The decrease in the number of
AgNPs in the 1-5 nm size bins was also significant, namely from 33.2% for the
original Creighton colloid down to 21.3% for the final 100-kD suspension.
Consequently, the average AgNP diameter increased from 9.3 nm for the original
Creighton colloid to 11.1 nm for the final 100-kD colloidal suspension.
Overall, the TEM results demonstrated that the three-step TFU process
was extremely effective in the size selection of AgNPs of 1-25 nm in diameter.
This isolation procedure can be tuned to further size select or narrow the size
distribution range of AgNPs by using different combinations of filtration modules
(pore sizes from 1,000-kD down to 10-kD and surfaces areas from 370 cm2 down
to 5 cm2). TFU eliminates the need of using capping/functionalizing agents to
better control the size and aggregation state of nanoparticles during their
synthesis.
59
Elemental analysis characterization using SEM-EDX
Figure 21 shows three SEM images that were collected from the final 100-
kD retentate of colloidal AgNPs deposited onto a carbon stub.
Figure 21: SEM-EDX results for 100-kD retentate of AgNPs. A, B, and C represent the SEM images with an acceleration voltage of 10 kV for A and B and
15kV for C. D depicts the EDX spectrum corresponding to image C.
It is evident from the SEM images that nanoparticles in the 0-50 nm size
range are not readily distinguishable at either magnification level. The images
most likely reflect the presence of large aggregates formed during the sample
preparation process. Four distinct peaks were observed in the EDX spectrum
(Figure 21D) between 2.5 and 3.5 keV. These peaks are characteristic of the
1000 nm 200 nm
A B
C D
2000 nm
60
binding energies associated with the AgLl, AgLa, AgLb and AgLg61-62. Two other
significant peaks were also present in the spectrum, namely CKa (0.25 keV), SKa
(2.3 keV) and OKa (0.57 keV). The CKa peak was attributed to the carbon
mounting stub, whereas the sulfur and the trace amount of oxygen detected may
be residual contamination from the polysulfone filtration module used in the TFU
process. Table 3 showed that the 100 kD retentate sample of AgNPs contained
mostly silver (60.1%) and smaller amounts of carbon and sulfur (29.6% and
8.7%, respectively). The weight ratio of silver to sulfur was found to be 6.9 : 1.0.
Table 3: Percentage weight of elements present in the final 100-kD retentate of AgNPs as revealed by EDX analysis.
Element Wt.%
CKa 29.6
Oka 3.0
Ska 8.7
AgL 60.1
The SEM-EDX results confirmed that the TFU process produced a final
100-kD retentate comprising of primarily silver with a minimal amount of sulfur
contamination resulting from the TFU process.
61
Micro-Raman Spectroscopy
Original Creighton colloid
The purity of the Creighton colloid was verified through micro-Raman
spectroscopy (Figure 22).
Figure 22: Raman spectrum of AgNP colloid.
Only two Raman peaks characteristic to the bending (1640 cm-1) and stretching
(3240-3420 cm-1) vibrational modes of H2O were observed in the Raman spectra,
which shows that the AgNP colloid is free of organic contaminants.
62
100-kD final retentate (1000 g mL-1)
One hundred micro-Raman point spectra were collected on the 100-kD
retentate of AgNPs. Visual inspection of the individual spectra revealed a high
degree of spectral reproducibility and similarity. Because of the similarities in
these Raman spectra, PCA was conducted to determine the most representative
spectra. Three PCs were identified and accounted for 99.6% of the cumulative
variance of the hyperspectral dataset (Figure 23).
Figure 23: Micro-Raman spectrum containing the three principal components accounting for 99.6% of the variance in the hyperspectral dataset (N = 100 spectra).
The largest portion of the variance was attributed to PC1 (90.6%). The
PC2 and PC3 contributions to the cumulative variance were much smaller (4.8%
63
and 4.2%, respectively). All other PCs contributed less than 1% to the cumulative
variance of the dataset and were discarded as noise-related components. All PC
Raman spectra contain a weak, broad band at 220 cm-1 probably due to an Ag-
S or Ag-O stretching mode63,64. This result is consistent with the SEM-EDX
finding of a minimal amount of sulfur present in the 100-kD final retentate. The
broad peak in between 1200 and 1700 cm-1 is characteristic to amorphous
carbon53 that is associated with the decomposition of organic matter under the
laser light (e.g., polysulfone membrane residues or organic impurities from air).
The micro-Raman spectra of PC1 and PC3 exhibited an additional weak feature
at 993 cm-1, which may be attributed to a ring breathing mode33 of the
ultrafiltration membrane residues.
Micro-Raman spectra confirmed the purity of the original Creighton colloid.
A single contaminant band was observed in the control micro-Raman spectra of
the predominant PC of the final 100-kD retentate probably coming from TFU
residues. This is extremely encouraging because the SERS spectra collected on
the VV incubated with AgNPs from 100-kD concentrate will exhibit minimum
interferences from the AgNPs alone.
Overall, the modified Creighton method for the synthesis of AgNP proved
to be an effective technique for the fabrication of non-functionalized small,
spherical, polydispersed AgNPs. Furthermore, large volumes can be synthesized
that will remain stable for up to six months with refrigeration. The TFU process
provides an efficient means by which to limit the polydispersity, size select to a
64
narrow distribution and concentrate AgNP suspensions without the addition of
aggressive solvent systems or capping agents. The final concentration ranges
can be tailored somewhat by varying the volume of the original Creighton AgNP
processed through the first step of the TFU process. In general however
extremely concentrated 100-kD suspensions from the final TFU step have a
relatively short shelf-life (<2 weeks) due to the large number of particles present
in a very small volume. This requires diligent preparation and careful project
planning in order to maximize the effective use of the AgNP suspension before
aggregation occurs.
Specific Aim 2:
Vaccinia Virus Control
Background information about Vaccinia Virus (VV)
VV is an enveloped virus that contains a dsDNA genome approximately
200 kb long11. The general shape of a mature virion is described as ellipsoidal
with dimensions65 of 360 nm x 270 nm x 250 nm.
Mature virions (MV), the simplest form of VV, consist of a lipid bilayer
external membrane that surrounds the lateral bodies and border the biconcave
core containing the double-stranded genomic DNA11. Figure 24 is a pictorial
representation of a mature VV.
65
Figure 24: Pictorial representation of the vaccinia virus.
The external membrane of the VV houses approximately 25-30 different
proteins11. Nine of these proteins, namely A21, A28, G3, H2, L5, A16, G9, J5 and
O3 have been identified as being a part of a well-organized and stable formation
of proteins termed the entry-fusion complex (EFC)35, 66. Three other EFC
associated proteins have been identified, specifically F9, I2 and L1, but they do
not appear to be integral to the formation or stabilization of the EFC67. F9 and I2,
in particular, assume many of the key functions of the EFC but efforts to verify
the proteins’ integral part of the complex remain inconclusive68-69. Three of the
EFC proteins (A16, G9 and J5) have similar amino acid (AA) sequences
Surface proteins Lateral bodies
Outer membrane
Inner membrane
Core wall
Core
ds genomic DNA
EFC proteins
66
including a C-terminus trans-membrane domain35, numerous invariant cysteine
groups11 and disulfide bonds (4-10)35. The A16 and the G9 protein in this group
are also myristoylated proteins, i.e., a myristic acid is bound to the glycine
terminus of the protein (Figure 25)11.
Figure 25: Myristoylated terminal moiety of a protein.
In contrast, the other six proteins (A21, A28, G3, H2, L5 and O3) are
heterologous with N-terminus trans-membrane domains, contain fewer invariant
cysteine groups or disulfide bonds (0-2)35, 66 and do not present myristoylated
moieties11. Table 4 summarizes several of the most significant structural
differences of the nine EFC proteins in terms of the presence of myristoylated
glycine AA N-terminus residues, trans-membrane terminus AAs (AAs), the
number of cysteine groups, the intramolecular disulfide bonds and the number of
aromatic AAs. These structural differences were emphasized in this study
because of their high abundance of electronegative atoms (e.g., O, N, and S)
and the possible interaction with electron withdrawing AgNPs.
Glycine
Myristic Acid
NH
H2C
O
A1A2A3...
O
67
Table 4: Structural differences in the nine EFC proteins relevant to the interaction mechanism with AgNPs. The total number of AAs in the sequence of each EFC protein is given in parenthesis.
Protein (# AAs)
Terminus Atom
# of Cysteine Groups
# of Disulfide Bonds
# of Aromatic Amino acids
Reference
A21
(117) N 5 2 20 70
A28
(146) N 5 2 25 71
G3
(111) N 1 0 18 72
H2
(378) N 5 2 28 73
L5
(133) N 2 1 27 74
O3
(35) N 1 0 9 66
A16 *
(378) C 20 8-10 77 75-76
G9 *
(340) C 14 4-7 54 35, 76-77
J5
(133) C 8 4 18 35
* denotes the presence of a myristoylated glycine residue.
All of the EFC proteins are integral membrane proteins, which may
transverse the external membrane of the virus at least once if not multiple times.
The AA constituents of the proteins that are located externally to the membrane
68
will be the first to encounter a host cell and likewise the first to interact with
AgNPs. A comprehensive look at these AAs is summarized in Figure 26.
Figure 26: A summary of the AA composition of the nine EFC proteins residing outside of the external membrane of VV.
The individual proteins are responsible for the formation of the EFC, its
fusion to the host cell membrane and the entry of the viral DNA into the host cell.
All of the EFC proteins participate in the fusion and entry functions of the
complex while A28, H2 and O3 are integral to the EFC formation35,66,73. Although
the complex structure of the EFC remains unknown, three pair-wise interactions
have been identified: A28 and H273, A16 and G9, and G3 and L567. The role of
69
one EFC protein J5 remains unknown; however, attempts to isolate a mutant
without the J5 protein have been unsuccessful35.
Estimation of the VV external surface coverage with AgNPs
The VV stock contained 1012 plaque forming units (PFUs) or virions per 1
mL of virus stock. By calculating the surface area of a mature VV, the number of
AgNPs needed to form a monolayer of coverage may be estimated. The
dimensions of a mature VV reported above are characteristic of a scalene
spheroid of three non-equal spatial dimensions. Making the assumption that
some degree of variability will be present in the proportions of the individual
virions, the dimensions describing a prolate spheroid or 360 nm x 260 nm x 260
nm were used to greatly simplify the surface area (SA) calculations (eq 13).
(
)
(13)
The SA of a VV was determined using two equal spatial dimensions, a = b = 260
nm, and c = 360 nm as follows:
(
)
(14)
The average AgNP diameter in the 100-kD colloidal concentrate was determined
to be 11.1 nm (r = 5.55 nm) from the TEM size histogram. Assuming a spherical
70
shape, the average volume and surface area for a single AgNP was calculated
as follows (eq 15 and 16).
(15)
(16)
For a 1 mL stock containing 1012 PFU, the number of AgNPs needed for
monolayer coverage was estimated as follows:
(17)
(
)
(18)
Using the silver density78,
(
)
and
the VAgNP, the silver mass of a single AgNP was determined (eq 19):
(
) (19)
For the AgNP treatment condition of 1,000 g mL-1, the number of AgNPs
present in 1 mL was found (eq 20):
71
(20)
This represents approximately 48,000 times the number of AgNPs needed to
achieve monolayer coverage in 1 mL of the viral stock solution (eq. 21).
(21)
The concentration of 1000 g mL-1 employed in this study was significantly higher
than the lowest inhibitory concentration (IC50 = 48 μg mL-1) reported for AgNPs
with VV5. However, monolayer coverage of AgNPs will greatly enable the SERS
detection of the silver covalent bonding interactions believed to responsible for
the viral inhibition mechanism.
Elemental analysis characterization using SEM-EDX
Figure 27 displays three SEM images of the VV control that was
deposited onto a copper stub for microscopy measurements. The first SEM
image (Figure 27a) depicts a large cluster of virions. A closer examination of the
image revealed the presence of several ellipsoidal particles with at least one
dimension between 200 – 300 nm. This size is consistent with the approximate
dimensions of VV65 of 360 nm x 270 nm x 250 nm. However, attempts to focus
upon and completely resolve the individual virions proved very difficult. This was
probably due to a thin biofilm covering the sample, which was also visible in the
72
first image. The SEM image in Figure 27b illustrates an individual virion with
measured dimensions of 352 X 288 nm.
Figure 27: SEM-EDX results for VV control. A, B, and C represent SEM images collected with acceleration voltages of 15 kV, 20 kV, and 20 kV, respectively. D
depicts the EDX spectrum corresponding to image C.
The EDX spectrum of the VV control which corresponds to the SEM image
in Figure 27c is shown in Figure 27d. The major chemical components of the
sample are carbon and oxygen as indicated by the large CKa peak at 0.26 keV
and the smaller OKa band at 0.52 keV, respectively. A trace amount of calcium
was also detected, which is mostly likely a contaminant from the PBS buffer. The
large copper binding energies are from the copper mounting stub. The relative
200 nm 200 nm
A B
C D
50,000 nm
73
weight percent (Wt%) for each element was included in Table 5. The Wt%
findings confirmed the predominance of carbon (55.8%) and oxygen (22.2%).
The a carbon to oxygen weight ratio was approximately 2.5 : 1.0. However, the
most significant result of this set of SEM-EDX measurements was the absence of
any silver in the VV control.
Table 5: Percentage weight of elements present in the VV control sample as revealed by the EDX analysis.
Element Wt.%
CKa 55.8
Oka 22.2
Caka 2.1
CuK 19.7
Micro-Raman Spectroscopy
Over 100 point spectra were collected from various locations on the VV
control sample to ascertain whether any appreciable signal could be detected
without the enhancement benefits of SERS. Figure 28 depicts 20 point spectra
for the VV control compared to a point spectrum taken from a control glass slide.
74
Figure 28: Micro-Raman point spectra (N = 20) of virus control samples in comparison with a point spectrum collected from the glass microscope slide control.
All VV control micro-Raman spectra were similar to the point spectrum collected
from the glass microscope slide. This confirmed the low sensitivity of the Raman
spectroscopy method at these analyte concentrations and further encouraged the
planned SERS measurements with AgNPs.
Vaccinia Virus treated with 1,000 g mL-1 of AgNPs
Elemental analysis
Three SEM images of the VV-AgNP sample deposited onto an aluminum
stub are shown in Figure 29.
75
Figure 29: SEM-EDX results for VV- AgNP sample. A depicts the electron backscatter SEM image using an acceleration voltage of 15 kV. B, C and D
represent SEM images collected with an acceleration voltage of 15 kV and the corresponding EDX spectrum for the VV-AgNP control, respectively.
The first frame (Figure 29a) depicts an SEM image collected from
backscattered electrons. With electron backscattering, heavier elements such as
silver will produce a larger angular deflection and will appear brighter in the
corresponding images40. In the above image, the presence of AgNP in the rinsed
VV-AgNP sample strongly suggests that the AgNPs are attached to the VV
sample. The presence of the silver also improved the conductivity of the sample
resulting in the ability to obtain a greater degree of resolution for the 20 electron
detector image (Figure 29b). Consequently, several spheroidal particles (red
A B
C D
10 m 1 m
100 m
76
circles) were observed and appeared to have relatively similar sizes. ImageJ
software was used to measure the largest dimension of each spheroid. The size
distribution for these spheroids ranged from 278 – 392 nm. The random
distribution of particles having dimensions consistent with that of VV suggests
that the observed particles correspond to individual virions.
Figure 29d depicts the EDX spectrum obtained for the VV-AgNP sample in
Figure 29c. The predominant peaks in the spectrum correspond to the AgLa and
AgLb binding energies of silver (between 2.5 and 3.5 keV), 61-62 and the Alka peak
associated with the aluminum mounting stub (1.5 keV). Silver and Al were
represented 31.4 % and 34.1%, respectively, by weight (Table 6).
Other significant peaks were CKa (16.6%), OKa (10.9%), NaKa (2.3%), MgKa
(2.4%), SKa (1.6%) and ClK (0.7%). The carbon and oxygen moieties may be due
to AA residues present in the VV component. Trace amounts of PBS solution
remaining after the rinsing step may result in the presence of Na, Mg and Cl
peaks.
77
Table 6: Percentage weight of elements present in the VV-AgNP sample as revealed by EDX analysis.
Element Wt.% for elements
CKa 16.6
OKa 10.9
NaKa 2.3
MgKa 2.4
AlKa 34.1
SKa 1.6
ClK 0.7
AgL 31.4
The electron backscatter images and the EDX spectrum of the VV-AgNP
sample revealed the presence of silver and further supported the direct
interaction between AgNPs and VV.
Background Information on Surface-enhanced Raman spectroscopy on
Viruses
SERS has become an increasingly popular analytical tool for the detection
and study of biological molecules. Pavel et.al (2008) used SERS to investigate
two FynSH3 proteins modified with double-cysteine groups designed to act as bi-
78
functional linkers to colloidal AgNPs. The two respective mutant proteins were
treated with a reducing agent to break the disulfide bonds and then incubated
with Creighton colloidal AgNPs53. The resulting SERS spectra revealed a Ag-S
peak at 167 cm-1 and a very strong C-S peak at 677 cm-1 indicating the
successful cleavage of the S-S bonds and the subsequent Ag-S covalent bond
formation53. Numerous interactions in between AgNPs and aromatic AAs were
also observed specifically for tryptophan (707, 1084, 1132, and 1243 cm-1),
phenylalanine (1183 and 1591 cm-1) and histidine (1399 and 1591 cm-1)53.
Stewart and Fredericks (1999) employed SERS to interrogate the
interaction mechanism between various di- and tri-peptides and an
electrochemically nanoroughened silver surface. The SERS spectra showed that
the primary mode of interaction between the peptides and the silver substrate
was through the carboxylate terminus of the AA chain32. This interaction was
confirmed through the presence of bands of high relative intensity, which were
associated with the carboxylate moieties, the peptide bond (amide peaks) and
the AA functional groups located in the close proximity of the carboxyl terminus32.
Furthermore, the vibrational modes that were attributed to the functional groups
closest to the amine terminus of the polypeptide were weaker or nonexistent.
This suggested that the amine group was further away from the silver
substrate32. Two AA samples containing thiol groups were also investigated to
determine if any Ag-S interactions may occur. In both cases the carboxyl group
bands had higher relative intensity than the thiol group suggesting a preferred
79
nanosubstrate interaction with the carboxylic acids32. When the carboxyl group
was not present, the interactions involved mostly aromatic AAs as opposed to
smaller side chains32. It seems that an interaction trend with the silver
nanosubstrate emerges here: carboxylic groups > peptide bond interactions
(amide peaks) > aromatic AAs > thiol groups > small side chains. No explanation
was provided in this regard.
Taking into account that well-established, viral detection methods such as
polymerase chain reaction (PCR) assays79 are time consuming and expensive,
recent studies have evaluated SERS as a potential alternative method. In order
to obtain highly reproducible spectra, most of the studies used solid-state
nanomaterial substrates. Colloidal nanomaterials have also been utilized but to a
smaller extent. Table 7 lists the six SERS studies that were performed on viruses
in the last two decades. Four of these studies utilized silver nanosubstrates
(silver nanorods and silver hydrosols)57,79-81, while the other two studies utilized
gold substrates (Kharite SERS active substrate and focused ion beam (FIB)-
fabricated nanosubstate)82-83.
Given the AA rich environment of the external membranes of viruses,
interactions were observed with all SERS substrates. The primary interactions
present were with carboxylate, amide, aromatic AA especially tryptophan,
tyrosine and phenylalanine and thiol groups. The SERS vibrational modes
characteristic to these interactions are summarized in Table 8.
80
Table 7: Viruses investigated by SERS and the used nanosubstrate.
Virus Abbreviation Virus type SERS substrate
Norovirus strain MNV-4 MNV482
Non-enveloped Kharite SERS active substrate: a silicon wafer coated with gold. A 6 x 10 mm
chip was attached to a glass slide. The
chip included a 4 x 4 mm patterned SERS active area and an unpatterned gold reference area.
Adenovirus strain Mad-1 MAD82
Non-enveloped
Parvovirus strain MVMp MVM82
Non-enveloped
Simian rotavirus strain SA-11 SA11
82 Non-enveloped
Coronavirus strain MHV-A59 MHV
82 Enveloped
Sendai virus strain Sendai82
Enveloped
Herpesvirus strain Smith MSGV MCMV
82 Enveloped
Influenza A WSN33 H1N1 H1N183, 79
Enveloped
FIB-fabricated Au nanosubstrate: 500
nm Au substrate mounted to silicon
with titanium adhesive.
Yaba monkey tumor virus YMTV80
Enveloped
Silver nanorods: Rod length of 868 nm ± 95
nm; Diameter of 99 nm ± 29 nm; Average
tilt angle of 71.3o ±
4.0 oC
Pseudocowpox PCPV80
Enveloped
Bovine papular stomatitis BVSV80
Enveloped
Purified Respiratory syncytial virus (RSV)
RSV79
Enveloped
RSV strain A2 A279, 57
Enveloped
RSV strain A/Long A/Long79, 57
Enveloped
RSV strain B1 B179, 57
Enveloped
RSV strain A2 with the deletion of the G gene
∆G79, 57
Enveloped
Adenovirus type 6/tonsil strain 99 AD
79 Enveloped
Rhinovirus type 4/strain 16/60 Rhino
79 Enveloped
HIV CXCR4-tropic strain HIV79
Enveloped
Influenza A HKx31 HKx3179
Enveloped
Influenza A PR/8/34 PR879
Enveloped
Trabala vishnou NPV TVP81
Enveloped Silver hydrosols: Synthesized using the
Creighton method. Dendrolimus punctatus NPV DPV81
Enveloped
Tobacco mosaic virus TMV84
Non-enveloped TERS: 20 nm silver
coated tip
81
Table 8: Literature assignment of the SERS peaks observed during the interaction of various viruses with silver and gold nanosubstrates.
Raman Shift (cm-1
) Assignment Virus
238, 244 (Ag-N) DPV(s), TPV(vs)
276 aliphatic chain C-C bending [δ(C-C)]
YMTV(m), PCPV(w), BVSV(s)
441, 456 rocking of COO- TPV(vw), DPV(vw)
455 (S-S) YMTV(vw), PCPV(vw), BVSV(vw)
521 skeletal stretching BPMV(vw)
52679
, 540, 54679
(S-S) MAD(w), MVM(m), SA11(w), BVSV(vw),
RSV(w, w-sh)
577 (COO-) TPV(m)
592 glycine HKx31(vw), PR8(vw), H1N1(vw)
612 δ(COO-) DPV(vw)
63379
,63579
, 64082
tyrosine (skeletal) A/Long(w-b), HKx31(vs), PR8(vs),
H1N1(vs), MAD(w), MVM(vw), SA11(vw)
76184
cysteine TMV(w)
83779
, 84482
, 84879
tyrosine MAD(vs), MVM4, SA11(w), Sendai(m),
MCMV(vs-b), MVM(s), MVH(vs), HIV(vw), RSV(m),
853 [(CCH)], tyrosine YMTV(w), PCPV(vw-b), BVSV(m),
Rhino(w)
877 tryptophan A/Long(vw), BPMV(vw), TMV(vw)
91681
, 92182
, 92781
, 932
84, 937
82, 943
82
(C-COO-), alanine)
DPV(vw), MNV4(s), MVH(w), Sendai(vw), MCMV(vw), TPV(m),TMV(vw)
1001, 1002, 1003, 1005
phenylalanine (symmetric ring breathing mode)
MVH(vw), Sendai(m), MCMV(vw), YMTV(m), PCPV(w), BVSV(s), AD(vw), Rhino(vw), HIV(vw), HKx31(w), PR8(w),
H1N1(w), TMV(ms)
101882
, 102282
, 1030
79, 1033
79
in-plane (C-H), phenylalanine
MVM(vw), MAD(vw), MCMV(m), MVH(w),
Sendai(m), AD(vw), Rhino(vw)
103181
, 103784
(C-N) DPV(vw), TMV(vw)
104279
, 104479
, 1045
79,1047
82, 79
105579
(C-N)
MCMV(m), MVM(w), MAD(vw), SA11(vs), MVH(w), Sendai(w), RSV(vs), HKx31(w),
PR8(w), H1N1(w), A/Long(vs), B1(vs), ∆G(vs), A2(vs)
1085 (C-N) TPV(m)
82
109884
PO2- stretch, alanine TMV(vw)
1122 Ʈ(NH2) DPV(vw)
1129 (C-N) and (C-C)
MVM(vw), MAD(s), SA11(m), MVH(m), Sendai(m), MCMV(vw), HKx31(ms),
PR8(s), H1N1(s), TMV(w)
115684
, 115882
(C-C) TMV(vw), MAD(m), MCMV(vw)
116583
, 116880
, 1173
82, 1179
84
tyrosine, v(CN) H1N1(vw), YMTV(m-b), PCPV(vw-b),
BVSV(m), MVH(vw), Sendai(m), TMV(vw)
1190 tyrosine,
phenylalanine A/Long(w-b)
124684
amide III TMV(vw)
1248 δ(NH2) AD(m), DPV(vw)
1260 CH2 in-plane
deformation, tyrosine H1N1(w), HKx31(w), PR8(w), H1N1(w)
1278 amide III TMV(vw-s)
1296 CH2 deformation MAD(vw), MVM(vw), SA11(vw)
133284
Ʈ(CH2)), tryptophan, TMV(m)
135881
,137179
, 1396
81
vs(COO-), tryptophan DPV(vw), HIV(vw), TPV(w)
1401 vs(COO-), AAs H1N1(w)
144382
, 144879
, 1454
79, 1455
84,
145779
δ(CH2)
MNV4(s), MAD(m), MVM(vs), SA11(w), YMTV(w-b), PCPV(w-b), BVSV(w-b), AD(w), Rhino(w), HIV(w), RSV(m),
BPMV(m), TMV(s)
1480 amide II (coupling of C-N stretch and in-
plane bending of N-H) H1N1(vw-s)
1515 (N-H) H1N1(w), YMTV(m-b), BVSV(vw)
1520 sym(COO-) DPV(vw)
1523 Tryptophan
asym(COO-)
HIV(vw)
155584
, 155883
tryptophan TMV(w), H1N1(w)
157481
, 157679
, 1582
81
asym(COO-),
tryptophan, tyrosine DPV(vw), AD(m), TPV(w)
1597 tyrosine MAD(vw), Rhino(vs)
161583
tyrosine H1N1(vs)
165579
, 165684
amide I AD(w), TMV(vs)
167982
amide I, (C=O) MAD(vw)
83
Abbreviations key for band intensitites is as follows: vw - very weak, w - weak, m - medium, ms - medium strong, s - strong, vs - very strong, b - broad, and sh - shoulder.
Surface-enhanced Raman spectroscopy on Creighton silver nanoparticles
interacting with vaccinia virus
In this study, 12 SERS-maps (10x10 m to 40x40 m with 1 µm step size)
were collected on the Creighton AgNPs incubated with VV. These SERS maps
contained a total of 2600 spectra. After the application of the signal to noise
algorithm, 392 spectra were retained and compiled into a single hyperspectral
dataset. VCA was then used to select 50 representative EMs from the compiled
dataset. Pairwise correlations between each EM using a threshold of 85% further
reduced the dataset to 10 representative spectra that contained the most unique
spectral components. Of the ten remaining spectra, three were nearly identical to
other spectra and were discarded. One spectrum contained little spectral
information above the signal to noise threshold and was also eliminated leaving
six spectra in the final representative set (Figure 30). The peak assignments of
each of the six representative SERS spectra are provided in Table 9 (100 – 1000
cm-1) and Table 10 (1000 – 2000 cm-1).
84
Figure 30: Six representative normalized SERS spectra (A-F) as selected by VCA and correlation algorithms.
85
Table 9: Tentative assignment of the SERS peaks in the 100-1000 cm-1 spectral range of the six, representative EMs (denoted with A-F in Figure 30).
Representative Endmembers (Ems)
A B C D E F Band Assignment
154
(Ag-S)53
194
(Ag-Ag)85
229 238 238 234 230 232 (Ag-N), (Ag-O)86-87
334 326 336
(Ag-N)88-89
395
392
394 Chain vibrations of myristic acid34
445 458 473, 500 466
(S-S)80,33,27
482
488
δ(C-C=O)33
504
(S-S)33
540
δ(CC3) deformation34
569
(COO-)81
615
δ(COO-)81
622
625
In plane δ(C=C) ring of tryptophan87,34
640
Ring deformation of tyrosine34
662
647
(C-S)32
674
(C-C)32
676
680
δ(O=C-O)33
727 727
694 δ(O=C-N) of amide IV33
746
Tryptophan32
775
Glutamine32
819 828
831 828 816 δ(C-SH)90
886 841
s(C-N-C)33
909
937
(C-COO-)32
955
Histidine34
964
Valine34
970
(C-C)90
994
Symmetric ring breathing mode of phenylalanine
90
86
Table 10: Tentative assignment of the SERS peaks in the 1000-2000 cm-1 spectral range of the six representative EMs (denoted with A-F in Figure 30).
Representative Endmembers (EMs)
A B C D E F Band Assignment
1009
Tryptophan32
1023
Alanine34
1035 1030
In plane (CH) of phenylalanine32
1043
1063
Histidine34
1100
1095
1088
(CN)32
1116
1116 Tryptophan34
1148
1139 (NH3+)32
1182
Out of phase (CCC)80
1190
Valine34
1210
1228 1229 1222
Pyrrole ring stretch91
1245 (NH2)32
1254 1253
(CH2)32
1267
Tyrosine34
1294
Amide III33
1298
(C-C) of myristic acid34
1302
1306
(CH2)33
1326 1322
Tyrosine34
1348 Histidine34
1336 Tryptophan33
1387 1384
Serine34
1411
1404
s(COO-)32
1430
Histidine34
1438
Myristic Acid34
1465 1451
1476 1477 δ(H-C-H)
1502
1531
1531
1539 (C-C(=O)-NH2) of amide II33
1563
Tryptophan91
1583 1572
Ring (C-C) of phenylalanine32
1592
Tyrosine32
87
Covalent bonding interactions of silver with vaccinia virus
Ag-N, Ag-O, and Ag-S interactions
Silver has a preferential attraction to electronegative atoms and tends to
covalently bond with oxygen, sulfur and nitrogen. Most of the representative
spectra (B, D, E, and F) exhibited a well-defined peak consistent with (Ag-N) or
(Ag-O)86-87 between 229 – 238 cm-1. Spectra A and C had a broader less
pronounced peak but the interaction was still present. Spectra B contained two
additional shoulder peaks at 154 cm-1 and 194 cm-1. The vibrational mode at 154
cm-1 is consistent with (Ag-S) interactions associated with large protein
structures,53 whereas the band at 194 cm-1 may be attributed to (Ag-Ag)85 that
occurs in general at 192 cm-1.
Carboxylic acid, myristic acid, amine group and peptide bond interactions
The EFC proteins of VV are rich in carboxylic acids groups pertaining to
the AA constituents and the two myristoylated proteins A16 and G911. As
previously discussed, silver readily bonds with available carboxylic acid groups32.
1598
as(COO-)32
1614
Tyrosine32
1631
1631 Histidine34
1638
1639 1643 1655 Amide I32
88
This was evidenced by the presence of multiple vibrational modes characteristic
to carboxylic acids in the representative SERS spectra. The most prominent
carboxylic acid peaks appeared in the SERS spectra A and E at 1411 cm-1 and
1598 cm-1. These s(COO-) and as(COO-) stretchings dominated the spectrum
A. Only the s(COO-) was observed at 1404 cm-1 for spectrum E. Carboxylic
signals of lower relative intensity were detected at 569 cm-1 (medium strong in
spectrum B), 615 cm-1 (weak in spectrum C), 676 and 680 cm-1 (weak in spectra
A and E), and 937 cm-1 (weak in spectrum E). These peaks were attributed to
vibrational modes characteristic to terminal carboxylic groups, namely (COO-)
(spectrum B), (COO-) (spectrum C), (O=C-O) (spectrum A), and (C-COO-)
(spectrum E).
Interactions were also observed for the carbonyl group moieties of the
peptide bonds comprising the backbone of the protein chain. A few examples
include the (C-C=O) peak at 482 cm-1 in spectrum B and a weak band at 488
cm-1 in spectrum D. In addition, three other interactions with the myristic acid
constituents were detected. The corresponding carbon chain vibrations (N > 3)34
were visible at 395, 392 and 394 cm-1 for spectra B, D and F, respectively.
Spectra D also exhibited a very strong (C-C)34 peak at 1298 cm-1 and a strong
(H-C-H)33 band at 1438 cm-1.
In the SERS spectra of proteins, amide bond interactions are often
observed from the peptide bond formed between the carboxylic acid group and
89
the amine group of adjacent AAs. The associated vibrational modes, often
named amide I – IV, are characterized by a certain degree of carbonyl (C=O),
(C-N) and (N-H)33. Weak amide I bands, primarily associated with carbonyl
adsorption33, were observed in the 1638 – 1655 cm-1 spectral range for SERS
spectra B, D, E and F. The spectra A, C and F presented amide II peaks mostly
represented by (N-H)33. This bending appeared at 1531 (medium strong) 1539
cm-1 (medium strong), and 1531 cm-1 (weak) in spectra A, C, and F, respectively.
The amide III mode derives much of its character equally from the carbonyl
adsorption and the N-H bending33. It was found at 1294 cm-1 as a medium
intensity band in spectrum C. Finally, the amide IV contributions were observed
as (O=C-N) bands at 727 cm-1 (weak in spectra A and B) and 694 cm-1 (very
strong in spectrum F). Weak s(C-N-C) modes were present at 886 cm-1 and 841
cm-1 in spectrum B and C, respectively, but could not be associated with a
specific amide band interaction.
Amine group interactions were only present in two of the representative
spectra (B and F). Spectrum F exhibited weak bands for (NH3+) and (NH2)
32 at
1139 and 1245 cm-1, respectively, whereas spectrum B had a weak intensity
(NH3+) peak at 1148 cm-1.
90
Aromatic amino acid interactions
Five AAs, histidine, phenylalanine, tyrosine, tryptophan and proline
contain aromatic ring-structure side chains. Of those, phenylalanine and tyrosine
have benzene rings, histidine has an imidazole ring , tryptophan contains an
indole group and proline forms an aliphatic ring between the amine group and the
-carbon of the AA (pyrrolidine ring)92-93. SERS studies confirm that AgNPs are
bind readily with the electron rich side chains of these AAs32, 91.
Several aromatic AA interactions were observed in the representative
spectra. The strongest bands (medium strong in intensity) associated with
tryptophan were found in SERS representative spectra D and F at 1116 cm-1
and in spectrum A at 1563 cm-1. Several weak tryptophan interactions were also
detected in the SERS spectra, specifically 1009 cm-1 for A, 622 cm-1 for B, and
746 and 1336 cm-1 for C. Weak tyrosine vibrational modes were observed in
SERS spectra A (1326 cm-1), B (1322 cm-1), C (640 cm-1 and 1614 cm-1) and E
(1267 and 1592 cm-1). Three significant bands were found for histidine at 1063
cm-1 (medium strong in D), 955 cm-1 (medium in B), and 1430 cm-1 (medium in
C). In addition, weak intensity peaks were exhibited in spectrum F (1348 and
1629 cm-1) and spectrum A (1631 cm-1). The characteristic symmetric ring
breathing mode of phenylalanine90 at 994 cm-1 was present in SERS spectrum F
at medium intensity. The in plane (CH) modes32 was detected at 1035 cm-1 as a
medium intensity peak in A and weakly 1030 cm-1 in B along with a weak ring
91
(C-C) peak in spectrum C at 1583 cm-1. Finally, only one vibrational mode was
observed for proline. This peak associated with the pyrrole ring stretch91
appeared at 1228 cm-1 (medium in C), 1229 cm-1 (medium strong in D) and 1222
cm-1 (medium strong in E).
Cysteine group interactions
The EFC proteins in VV contain multiple, invariant cysteine AAs and
disulfide bonds. Direct interactions of EFC proteins with AgNPs through these
groups are also possible. All SERS spectra with the exception of spectrum C
presented weak (C-SH) modes in the 816 – 831 cm-1 spectral region consistent
with the functional group of cysteine90. Furthermore, spectra B and D exhibited
(C-S) at 662 cm-1 and 647 cm-1, respectively. Weak overtones of the (C-C-S)
band33 were detected at 326 – 336 cm-1 for spectra A – C.
In the EFC, 23 – 28 disulfide bonds are formed among the nine proteins35.
Evidence of the disulfide bonds was present in several of the representative
spectra. A very strong (S-S) mode was observed at 458 cm-1 in spectrum B,
while relatively weak bands were found at 445 cm-1 and 466 cm-1 in spectra A
and D respectively. Spectrum B also exhibits a second (S-S) peak at 504 cm-1
however this band is much weaker in intensity than the other peak. Spectra C
contained two very strong, distinct and nearly symmetrical (S-S) bands at 473
92
cm-1 and 500 cm-1. The nature and proximity of these bands in the spectrum
strongly suggests that AgNPs are covalently bonded to both electron rich sulfur
atoms. This unique NP interaction would result in peaks with similar
enhancement due to the sulfur atoms proximity to the AgNPs. However, the
bands would be observed at different wavenumbers due to the varying AA
constituents on the two different invariant cysteines.
SERS spectra correlation
The SERS results obtained in this work are consistent with those reported
in the few SERS literature studies on other viruses. Strong covalent interactions
were observed in between the Creighton colloidal AgNPs of 1-20 nm in diameter
and the same protein moieties of VV. Figure 31 illustrates that the number of
observed interactions was dominated by AA with aromatic functional groups and
similar to the other SERS studies, interactions with small side chains were
negligible. More exactly, the number of aromatic AA modes was observed
approximately 5 times more frequently (i.e., with a ratio of 26.0 : 5.0 or 5.2 : 1.0)
than smaller aliphatic side chain AAs. The predominance of aromatic AA modes
is not surprising as these AA side chains tend to be oriented towards the outside
of the protein structures (e.g., helixes, sheets, turns and folds) to reduce the
steric hindrance of the overall conformation92. As a result, AgNPs may come in
contact with these electron rich moieties more readily.
93
Figure 31: Six representative normalized SERS spectra (A-F) together with the primary interactions (carboxylic groups, amide groups, aromatic amino acids and thiol groups) ranked in order from most frequent to least often.
1) Carboxylic
2) Aromatic
3) Thiol
4) Amide
1) Aromatic
2) Amide
3) Carboxylic
4) Thiol
1) Aromatic
2) Carboxylic
3) Amide
4) Thiol
1) Carboxylic
2) Aromatic
3) Thiol
4) Amide
1) Aromatic
2) Carboxylic
3) Amide
4) Thiol
1) Aromatic
2) Amide
3) Carboxylic
4) Thiol
94
Aromatic AAs, carboxylic group and amide bond modes were far more
prevalent numerically than thiol modes. AgNP interactions with carboxylic
moieties were observed more frequently than amide and thiol group interactions
indicating the preferential binding of AgNPs for the carboxylic terminus of the
protein versus the amine group terminus or the sulfur groups.
The interaction trend revealed by the analysis of the six EM spectra
seems to be slightly different than observed in the previous SERS studies on
other viruses: aromatic AAs > carboxylic groups> amide groups> thiol groups >
small side chains. However, the carboxylic and amide modes were consistently
stronger in intensity suggesting a closer proximity and stronger bonding
interaction with the AgNPs. Furthermore, there are far more vibrational modes
possible for the side chains of the five aromatic AA acids than for the carboxylic
moieties. Considering 3N-6 vibrational degrees of freedom with N representing
the number of atoms present in the molecule, the simplest side chain of these
amino acids, benzene (N = 12) of phenylalanine, would have 30 vibrational
modes (i.e., ). Of those, 12 are Raman active46.
Comparatively, the carboxylic acid moiety of proteins exhibits six Raman
vibrations32. Therefore, it is not surprising that the aromatic AA moieties were
more numerous than the carboxylic modes and peptide bond interactions. For
these reasons, it is believed that the observed AgNP interaction trends in this
study are consistent with those reported in literature: carboxylic groups > peptide
95
bond interactions (amide peaks) > aromatic AAs > thiol groups > small side
chains.
After the SERS representative spectra were determined, the correlation
algorithm was employed a second time to determine the spectral similarity of the
six representative EMs to the entire compiled hyperspectral dataset (392 spectra)
using the same correlation thresholds (Table 11).
Table 11: Spectral correlation of EMs A-F to the compiled, hyperspectral dataset (392 spectra). The values represent the number of spectra in the compiled dataset that contain similar spectral characteristics to the representative EMs at specific correlation thresholds.
Since the correlation coefficient algorithm compares each pixel of the
spectral pairs, two spectra that were identical would correlate at 100%.
Therefore, most of the EMs were only correlated to one spectrum in the dataset
(i.e., the actual EM) at very high thresholds of 90% or high. Since many the
spectra contain similar vibrational features, any one member of the dataset may
Correlation Threshold (%)
EM 95 90 85 80 75
A 1 1 4 53 150
B 3 24 61 106 166
C 1 1 1 1 1
D 1 1 3 33 88
E 1 1 18 71 144
F 1 1 19 55 88
96
correlate to more than one EM especially at lower threshold values. For this
reason, this analysis cannot definitely describe the spectral correlation for all
members of the dataset. If an EM has very unique spectral features it will
correspond to fewer dataset members even at lower thresholds. EM C only
correlated to itself even down to a 75% correlation threshold. This is most likely a
result of the very strong, nearly symmetrical (S-S) bands at 473 cm-1 and 500
cm-1. The most highly correlated spectra were EMs B, A and E. At a 75%
threshold, 166, 150 and 144 spectra from the compiled dataset were correlated
to the EMs, respectively. The remaining two EMs (D and F) are more unique
spectrally and therefore correlated to 88 dataset members at 75%. Cumulatively,
the total number of dataset members accounted for at 75% was 637, a number
that is well above the actual 392 spectra in the dataset. This confirmed the
prevalence of similar spectral features and consistent interaction patterns in
spectral dataset as a whole. However the representative SERS EMs also
contained many unique features. This suggested that a very large number of
spectra in the compiled dataset (if not all) may be accounted for by the
representative EMs.
97
Conclusions
There is an ever increasing need for the development of a broad-spectrum
antiviral drug that can be effective against a wide variety of viruses. AgNPs have
a demonstrated antiviral activity and offer a viable option as potential antiviral
agents. However, a more definitive mechanism of viral inhibition needs to be
defined. This study confirmed the proposed hypothesis and showed for the first
time that unfunctionalized, Creighton AgNPs of an average diameter of ~11 nm
bind covalently to the AA moieties present in the EFC proteins of VV. However,
given the abundance of proteins comprising the external membrane of VV (i.e.,
25-30), AgNP binding to other proteins on the external VV membrane cannot be
excluded. In this context, TFU demonstrated to be a “green” method for the size-
selection and extreme concentration of large volumes of colloidal AgNPs with
minimal aggregation.
Further research should focus on determining if any other steps of the viral
replication cycle of the VV are also disrupted by the AgNPs or if the AgNPs are
taken into the core of the VV. Given that VV does not have an active metabolism,
the later scenario is unlikely. Also different AgNP formulations and different size
distributions should also be investigated in a similar manner.
98
References
1. Kennedy, R. B.; Ovsyannikova, I.; Poland, G. A., Smallpox vaccines for
biodefense. Vaccine 2009, 27, D73-D79.
2. Behbehani, A. M., The smallpox story: life and death of an old disease.
Microbiological reviews 1983, 47 (4), 455-509.
3. Clark, W. R., "The demon in the freezer". Alaska medicine 2002, 44 (4),
103.
4. Rogers, J. V.; Parkinson, C. V.; Choi, Y. W.; Speshock, J. L.; Hussain, S.
M., A Preliminary Assessment of Silver Nanoparticle Inhibition of
Monkeypox Virus Plaque Formation. Nanoscale Research Letters 2008, 3
(4), 129-133.
5. Trefry, J. C. The Development of Silvernanoparticles as Antiviral Agents.
Doctoral Thesis, Wright State University, 2011.
6. Dal Lago, V.; Franca, d. O.; de, A. G.; Kobarg, J.; Borba Cardoso, M.,
Size-selective silver nanoparticles: future of biomedical devices with
enhanced bactericidal properties. Journal of Materials Chemistry 2011, 21
(33), 12267-12273.
7. Jain, J.; Arora, S.; Rajwade, J. M.; Omray, P.; Khandelwal, S.; Paknikar,
K. M., Silver Nanoparticles in Therapeutics: Development of an
99
Antimicrobial Gel Formulation for Topical Use. Molecular Pharmaceutics
2009, 6 (5), 1388-1401.
8. Elechiguerra, J.; Burt, J.; Morones, J.; Camacho-Bragado, A.; Gao, X.;
Lara, H.; Yacaman, M., Interaction of silver nanoparticles with HIV-1.
Journal of Nanobiotechnology 2005, 3 (1), 6.
9. Baram-Pinto, D.; Shukla, S.; Perkas, N.; Gedanken, A.; Sarid, R.,
Inhibition of Herpes Simplex Virus Type 1 Infection by Silver Nanoparticles
Capped with Mercaptoethane Sulfonate. Bioconjugate chemistry 2009, 20
(8), 1497-1502.
10. Bogdanchikova, N. E.; Kurbatov, A. V.; Tret'yakov, V. V.; Rodionov, P. P.,
Activity of colloidal silver preparations towards smallpox virus.
Pharmaceutical Chemistry Journal 1992, 26 (9-10), 778-779.
11. Condit, R. C.; Moussatche, N.; Traktman, P., In a nutshell: Structure and
assembly of the vaccinia virion. Advances in Virus Research 2006, 66, 31-
124.
12. Roberts, K. L.; Smith, G. L., Vaccinia virus morphogenesis and
dissemination. Trends in microbiology 2008, 16 (10), 472-9.
13. Pan ek, A. v tek, L. Prucek, . ol , M. Ve e ov , . Piz rov , N.
Sharma, V. . Nev n , T. . bo il, ., Silver Colloid Nanoparticles
Synthesis, Characterization, and Their Antibacterial Activity. The Journal
of Physical Chemistry B 2006, 110 (33), 16248-16253.
100
14. Bonnemann, H.; Richards, R. M., Nanoscopic Metal Particles −
Synthetic Methods and Potential Applications. European Journal of
Inorganic Chemistry 2001, 2001 (10), 2455-2480.
15. Kopeikin, V. V.; Panarin, E. F., Water-Soluble Nanocomposites of
Zerovalent Metallic Silver with Enhanced Antimicrobial Activity. Doklady
Chemistry 2001, 380 (4), 277-279.
16. Nowack, B.; Krug, H. F.; Height, M., 120 Years of Nanosilver History:
Implications for Policy Makers. Environmental Science & Technology
2011, 45 (4), 1177-1183.
17. Li, D.; Qu, L.; Zhai, W.; Xue, J.; Fossey, J. S.; Long, Y., Facile On-Site
Detection of Substituted Aromatic Pollutants in Water Using Thin Layer
Chromatography Combined with Surface-Enhanced Raman
Spectroscopy. Environmental Science & Technology 2011, 45 (9), 4046-
4052.
18. Li, Y.; Wu, Y.; Ong, B. S., Facile Synthesis of Silver Nanoparticles Useful
for Fabrication of High-Conductivity Elements for Printed Electronics.
Journal of the American Chemical Society 2005, 127 (10), 3266-3267.
19. Bhattacharya, . Mukher ee, P., Biological properties of “naked” metal
nanoparticles. Inorganic Nanoparticles in Drug Delivery 2008, 60 (11),
1289-1306.
101
20. Han, X.; Zhao, B.; Ozaki, Y., Surface-enhanced Raman scattering for
protein detection. Analytical and Bioanalytical Chemistry 2009, 394 (7),
1719-1727.
21. Willets, K., Surface-enhanced Raman scattering (SERS) for probing
internal cellular structure and dynamics. Analytical and Bioanalytical
Chemistry 2009, 394 (1), 85-94.
22. Lara, H. H.; Ayala-Nunez, N. V.; Ixtepan-Turrent, L.; Rodriguez-Padilla, C.,
Mode of antiviral action of silver nanoparticles against HIV-1. Journal of
Nanobiotechnology 2010, 8, 1-10.
23. Lu, L.; Sun, R. W.; Chen, R.; Hui, C. K.; Ho, C. M.; Luk, J. M.; Lau, G. K.;
Che, C. M., Silver nanoparticles inhibit hepatitis B virus replication.
Antiviral therapy 2008, 13 (2), 253-262.
24. Sopova, E. A.; Baranov, V. I.; Gankovskaia, O. A.; Lavrov, V. F.; Zverev,
V. V., Effects of silver and silicon dioxide nanopowders on the
development of herpesvirus infection in vitro. Gigiena i sanitariia 2010,
(4), 89-91.
25. Speshock, J. L.; Murdock, R. C.; Braydich-Stolle, L. K.; Schrand, A. M.;
Hussain, S. M., Interaction of silver nanoparticles with Tacaribe virus.
Journal of Nanobiotechnology 2010, 8 (19), 1-9.
26. Hussain, S. M.; Javorina, A. K.; Schrand, A. M.; Duhart, H. M.; Ali, S. F.;
Schlager, J. J., The Interaction of Manganese Nanoparticles with PC-12
102
Cells Induces Dopamine Depletion. Toxicological Sciences 2006, 92 (2),
456-463.
27. Biswas, N.; Waring, A. J.; Walther, F. J.; Dluhy, R. A., Structure and
conformation of the disulfide bond in dimeric lung surfactant peptides SP-
B1–25 and SP-B8–25. BBA - Biomembranes 2007, 1768 (5), 1070-1082.
28. Creighton, J. A.; Blatchford, C. G.; Albrecht, M. G., Plasma resonance
enhancement of Raman scattering by pyridine adsorbed on silver or gold
sol particles of size comparable to the excitation wavelength. Journal of
the Chemical Society, Faraday Transactions 2: Molecular and Chemical
Physics 1979, 75, 790-798.
29. Pavel, I. E.; Alnajjar, K. S.; Monahan, J. L.; Stahler, A.; Hunter, N. E.;
Weaver, K. M.; Baker, J. D.; Meyerhoefer, A. J.; Dolson, D. A., Estimating
the Analytical and Surface Enhancement Factors in Surface-Enhanced
Raman Scattering (SERS): A Novel Physical Chemistry and
Nanotechnology Laboratory Experiment. Journal of Chemical Education
2012, 89 (2), 286-290.
30. Anders, C. B.; Baker, J. D.; Stahler, A. C.; Williams, A.; Trefry, J. C.;
Wooley, D. P.; Sizemore, I. E. P., Tangential Flow Ultrafiltration A “Green”
Method for the Size Selection and Concentration of Colloidal Silver
Nanoparticles. Journal of Visualized Experiments 2012, In Press.
103
31. Trefry, J. C.; Monahan, J. L.; Weaver, K. M.; Meyerhoefer, A. J.;
Markopolous, M. M.; Arnold, Z. S.; Wooley, D. P.; Pavel, I. E., Size
Selection and Concentration of Silver Nanoparticles by Tangential Flow
Ultrafiltration for SERS-Based Biosensors. Journal of the American
Chemical Society 2010, 132 (32), 10970-10972.
32. Stewart, S.; Fredericks, P. M., Surface-enhanced Raman spectroscopy of
peptides and proteins adsorbed on an electrochemically prepared silver
surface. Spectrochimica Acta Part A: Molecular and Biomolecular
Spectroscopy 1999, 55 (7-8), 1615-1640.
33. Dollish, F. R.; Fateley, W. G.; Bentley, F. F., Characteristic Raman
frequencies of organic compounds. Wiley: New York,, 1974; p 443
34. De Gelder, J.; De Gussem, K.; Vandenabeele, P.; Moens, L., Reference
database of Raman spectra of biological molecules. Journal of Raman
Spectroscopy 2007, 38 (9), 1133-1147.
35. Senkevich, T. G.; Ojeda, S.; Townsley, A.; Nelson, G. E.; Moss, B.,
Poxvirus multiprotein entry-fusion complex. Proceedings of the National
Academy of Sciences of the United States of America 2005, 102 (51),
18572-18577.
36. Keshava, N.; Mustard, J. F., Spectral Unmixing. IEEE Signal Processing
Magazine 2002, 1, 44-57.
104
37. De Juan, A.; Tauler, R.; Dyson, R.; Marcolli, C.; Rault, M.; Maeder, M.,
Spectroscopic imaging and chemometrics: a powerful combination for
global and local sample analysis. Trends in Analytical Chemistry 2004, 23
(1), 70-79.
38. Amigo, J. M., Practical issues of hyperspectral imaging analysis of solid
dosage forms. Analytical and Bioanalytical Chemistry 2010, 398 (1), 93-
109.
39. Mil ković, M. Chernenko, T. omeo, M. J. Bird, B. Matthäus, C. Diem,
M., Label-free imaging of human cells: algorithms for image reconstruction
of Raman hyperspectral datasets. The Analyst 2010, 135 (8), 2002-2013.
40. Hornyak, G. L., Introduction to nanoscience & nanotechnology. CRC
Press: Boca Raton, 2009; p 1593.
41. Robinson, J. W.; Frame, E. M. S.; Frame, G. M., Undergraduate
instrumental analysis. 6th ed / ed.; Marcel Dekker: New York, 2005; p
1079.
42. Skoog, D. A.; Holler, F. J.; Crouch, S. R., Principles of instrumental
analysis. 6th ed.; Thomson Brooks/Cole: Australia ; Canada, 2007; p
1039.
43. Ershov, B. G.; Gordeev, A. V., Silver nanoparticles stabilised with
heteropoly anions in an aqueous solution: optical properties and electronic
polarisation. Mendeleev Communications 2001, 11 (4), 147-148.
105
44. Novak, J. P.; Nickerson, C.; Franzen, S.; Feldheim, D. L., Purification of
Molecularly Bridged Metal Nanoparticle Arrays by Centrifugation and Size
Exclusion Chromatography. Analytical Chemistry 2001, 73 (23), 5758-
5761.
45. Rasband, W. J. ImageJ. http://imagej.nih.gov/ij/.
46. Atkins, P. W.; DePaula, J., Physical chemistry. 7th ed.; W. H. Freeman:
New York, 2002; p 1139.
47. Silbey, R. J.; Bawendi, M. G.; Alberty, R. A., Physical chemistry. 4th ed.;
John Wiley & Sons: Hoboken, NJ, 2005; p 944
48. Siebert, F.; Hildebrandt, P., Vibrational spectroscopy in life science. Wiley-
VCH: Weinheim, 2008; p 310 p.
49. Fleischmann, M.; Hendra, P. J.; McQuillan, A. J., Raman Spectra Of
Pyridine Adsorbed at a Silver Electrode. Chemical Physics Letters 1974,
26 (2), 163-166.
50. Jeanmaire, D. L.; Duyne, R. P. V., Surface Raman
Spectroelectrochemistry: Part I. Heterocyclic, Aromatic, and Aliphatic
Amines Adsorbed on the Anodizded Silver Electrode. Journal of
electroanalytical chemistry 1977, 84, 1-20.
51. Xu, H.; Bjerneld, E. J.; Käll, M.; Börjesson, L., Spectroscopy of Single
Hemoglobin Molecules by Surface Enhanced Raman Scattering. Physical
Review Letters 1999, 83 (21), 4357-4360.
106
52. Nie, S.; Emory, S. R., Probing Single Molecules and Single Nanoparticles
by Surface-Enhanced Raman Scattering. Science 1997, 275 (21), 1102-
1106.
53. Pavel, I.; McCarney, E.; Elkhaled, A.; Morrill, A.; Plaxco, K.; Moskovits, M.,
Label-Free SERS Detection of Small Proteins Modified to Act as
Bifunctional Linkers. The Journal of Physical Chemistry C 2008, 112 (13),
4880-4883.
54. Gendrin, C.; Roggo, Y.; Collet, C., Pharmaceutical applications of
vibrational chemical imaging and chemometrics: A review. Journal of
pharmaceutical and biomedical analysis 2008, 48 (3), 533-553.
55. Lee, E.; Adar, F.; Whitley, A.; , Multivariate Data Processing of Spectral
Images: The Ugly, the Bad, and the True. Spectroscopy 2007, 9, 1-5.
56. Nascimento, J. M. P.; Dias, J. M. B., Vertex Component Analysis: A Fast
Algorithm to Unmix Hyperspectral Data. IEEE Transactions on
Geoscience and Remote Sensing 2005, 43 (4), 898-910.
57. Shanmukh, S.; Jones, L.; Zhao, Y. P.; Driskell, J. D.; Tripp, R. A.; Dluhy,
R. A., Identification and classification of respiratory syncytial virus (RSV)
strains by surface-enhanced Raman spectroscopy and multivariate
statistical techniques. Analytical and Bioanalytical Chemistry 2008, 390
(6), 1551-1555.
107
58. Doremus, R. H., Optical Properties of Small Silver Particles. The Journal
of Chemical Physics 1965, 42 (1), 414.
59. Mohapatra, S.; Siddhanta, S.; Kumar, D. R.; Narayana, C.; Maji, T. K.,
Facile and Green Synthesis of SERS Active and Ferromagnetic Silver
Nanorods. European Journal of Inorganic Chemistry 2010, 2010 (31),
4969-4974.
60. Pragatheeswaran, A.; Kareem, T. A.; Kaliani, A. A., Effect of plasma
exposure on silver nanoparticles embedded in polyvinyl alcohol. Journal of
Physics: Conference Series 2010, 208, 1-12.
61. Guzmán, M. G.; Dille, J.; Godet, S., Synthesis of silver nanoparticles by
chemical reduction method and their antibacterial activity. International
Journal of Chemical and Biomolecular Engineering 2009, 2 (3), 104-111.
62. Puchalski, M.; Dabrowski, P.; Olejniczak, W.; Krukowski, P.; Kowalczyk,
P.; Polanski, K., The study of silver nanoparticles by scanning electron
microscopy, energy dispersive X-ray analysis and scanning tunnelling
microscopy. Materials Science-Poland 2007, 25 (2), 473-478.
63. Niaura, G.; Jakubenas, R., The alkali metal cation effect on the surface-
enhanced Raman spectra of phosphate anions adsorbed at silver
electrodes. Journal of electroanalytical chemistry 2001, 510 (1-2), 50-58.
64. Sandroff, C. J.; Herschbach, D. R., Surface-enhanced Raman study of
organic sulfides adsorbed on silver: facile cleavage of sulfur-sulfur and
108
carbon-sulfur bonds. The Journal of Physical Chemistry 1982, 86 (17),
3277-3279.
65. Cyrklaff, M.; Risco, C.; Fernandez, J. J.; Jimenez, M. V.; Esteban, M.;
Baumeister, W.; Carrascosa, J. L., Cryo-electron tomography of vaccinia
virus. Proc Natl Acad Sci U S A 2005, 102 (8), 2772-7.
66. Satheshkumar, P. S.; Moss, B., Characterization of a newly identified 35-
amino-acid component of the vaccinia virus entry/fusion complex
conserved in all chordopoxviruses. J Virol 2009, 83 (24), 12822-32.
67. Wolfe, C. L.; Moss, B., Interaction between the G3 and L5 proteins of the
vaccinia virus entry–fusion complex. Virology 2011, 412 (2), 278-283.
68. Brown, E.; Senkevich, T. G.; Moss, B., Vaccinia virus F9 virion membrane
protein is required for entry but not virus assembly, in contrast to the
related L1 protein. Journal of virology 2006, 80 (19), 9455-9464.
69. Nichols, R. J.; Stanitsa, E.; Unger, B.; Traktman, P., The vaccinia virus
gene I2L encodes a membrane protein with an essential role in virion
entry. J Virol 2008, 82 (20), 10247-61.
70. Townsley, A. C.; Senkevich, T. G.; Moss, B., Vaccinia virus A21 virion
membrane protein is required for cell entry and fusion. J Virol 2005, 79
(15), 9458-69.
71. Senkevich, T. G.; Ward, B. M.; Moss, B., Vaccinia virus A28L gene
encodes an essential protein component of the virion membrane with
109
intramolecular disulfide bonds formed by the viral cytoplasmic redox
pathway. J Virol 2004, 78 (5), 2348-56.
72. Izmailyan, R. A.; Huang, C. Y.; Mohammad, S.; Isaacs, S. N.; Chang, W.,
The envelope G3L protein is essential for entry of vaccinia virus into host
cells. J Virol 2006, 80 (17), 8402-10.
73. Senkevich, T. G.; Moss, B., Vaccinia Virus H2 Protein Is an Essential
Component of a Complex Involved in Virus Entry and Cell-Cell Fusion.
Journal of virology 2005, 79 (8), 4744-4754.
74. Townsley, A. C.; Senkevich, T. G.; Moss, B., The Product of the Vaccinia
Virus L5R Gene Is a Fourth Membrane Protein Encoded by All Poxviruses
That Is Required for Cell Entry and Cell-Cell Fusion. Journal of virology
2005, 79 (17), 10988-10998.
75. Ojeda, S.; Senkevich, T. G.; Moss, B., Entry of Vaccinia Virus and Cell-
Cell Fusion Require a Highly Conserved Cysteine-Rich Membrane Protein
Encoded by the A16L Gene. Journal of virology 2006, 80 (1), 51-61.
76. Martin, K. H.; Grosenbach, D. W.; Franke, C. A.; Hruby, D. E.,
Identification and analysis of three myristylated vaccinia virus late
proteins. J Virol 1997, 71 (7), 5218-26.
77. Ojeda, S.; Domi, A.; Moss, B., Vaccinia virus G9 protein is an essential
component of the poxvirus entry-fusion complex. J Virol 2006, 80 (19),
9822-30.
110
78. Lide, D. R., CRC handbook of chemistry and physics, 2000-2001. 81st
ed.; CRC Press: Boca Raton, 2000.
79. Shanmukh, S.; Jones, L.; Driskell, J.; Zhao, Y.; Dluhy, R.; Tripp, R. A.,
Rapid and Sensitive Detection of Respiratory Virus Molecular Signatures
Using a Silver Nanorod Array SERS Substrate. Nano Letters 2006, 6 (11),
2630-2636.
80. Alexander, T. A., Development of Methodology Based on Commercialized
SERS-Active Substrates for Rapid Discrimination of Poxviridae Virions.
Analytical Chemistry 2008, 80 (8), 2817-2825.
81. Bao, P.-D.; Huang, T.-Q.; Liu, X.-M.; Wu, T.-Q., Surface-enhanced Raman
spectroscopy of insect nuclear polyhedrosis virus. Journal of Raman
Spectroscopy 2001, 32 (4), 227-230.
82. Fan, C.; Hu, Z.; Riley, L. K.; Purdy, G. A.; Mustapha, A.; Lin, M., Detecting
Food- and Waterborne Viruses by Surface-Enhanced Raman
Spectroscopy. Journal of Food Science 2010, 75 (5), M302-M307.
83. Lin, Y.-Y.; Liao, J.-D.; Ju, Y.-H.; Chang, C.-W.; Shiau, A.-L., Focused ion
beam-fabricated Au micro/nanostructures used as a surface enhanced
Raman scattering-active substrate for trace detection of molecules and
influenza virus. Nanotechnology 2011, 22 (18), 185308-185308.
84. Cialla, D.; Deckert-Gaudig, T.; Budich, C.; Laue, M.; Möller, R.; Naumann,
D.; Deckert, V.; Popp, J., Raman to the limit: tip-enhanced Raman
111
spectroscopic investigations of a single tobacco mosaic virus. Journal of
Raman Spectroscopy 2009, 40 (3), 240-243.
85. Weidlein, J.; Muller, U.; Dehnicke, K., Schwingungsfrequenzen II:
Nebengruppenelemente. Georg Theime Verlag Stutgart: New York, 1986;
p 206
86. Compagnini, G.; Pelligra, B.; Pignataro, B., A SERS study of 1-methyl-2-
pyrrolidinone adsorbed on active silver surfaces. Chemical Physics Letters
1998, 285 (3-4), 266-270.
87. Kundu, P. P.; Pavan Kumar, G. V.; Mantelingu, K.; Kundu, T. K.;
Narayana, C., Raman and surface enhanced Raman spectroscopic
studies of specific, small molecule activator of histone acetyltransferase
p300. Journal of Molecular Structure 2011, 999 (1-3), 10-15.
88. Sanchez, L. A.; Lombardi, J. R.; Birke, R. L., Surface enhanced Raman
scattering of ammonia. Chemical Physics Letters 1984, 108 (1), 45-50.
89. Sant'Ana, A. C.; Alves, W. A.; Santos, R. H. A.; Ferreira, A. M. D.;
Temperini, M. L. A., The adsorption of 2,2';:6',2';-terpyridine, 4';-(5-
mercaptopentyl)-2,2';:6',2';-terpyridinyl, and perchlorate on silver and
copper surfaces monitored by SERS. Polyhedron 2003, 22 (13), 1673-
1682.
90. Li, H.; Wurrey, C. J.; Thomas, G. J., Structural studies of viruses by laser
Raman spectroscopy. Part XXXVI. Cysteine conformation and sulfhydryl
112
interactions in proteins and viruses. 2. Normal coordinate analysis of the
cysteine side chain in model compounds. Journal of the American
Chemical Society 1992, 114 (19), 7463-7469.
91. Combs, A.; McCann, K.; Autrey, D.; Laane, J.; Overman, S. A.; Thomas,
G. J., Raman signature of the non-hydrogen-bonded tryptophan side chain
in proteins: experimental and ab initio spectra of 3-methylindole in the gas
phase. Journal of Molecular Structure 2005, 735-736, 271-278.
92. Berg, J. M.; Tymoczko, J. L.; Stryer, L.; Stryer, L., Biochemistry. 6th ed.;
W. H. Freeman: New York, 2007; p 1026.
93. Bruice, P. Y., Organic chemistry. 4th ed.; Pearson/Prentice Hall: Upper
Saddle River, NJ, 2004; p 1228.
